Tag Archives: Campaigns
4 Steps for Creating Engaging and Effective Lead Nurturing Campaigns
4) Score Your Leads Accurately for More Effective Sales Exploration
Every action your leads take (or, in some cases, don’t take) should influence their overall lead score. Once these contacts reach a certain score, you should have a mechanism in place to pass them along to Sales for more personalized outreach.
To get a clear picture of your leads’ intent, you should be paying close attention to email opens, clicks, conversions, unsubscribes, and other key performance indicators your team values. Having full visibility and understanding into these engagements is an essential part of accurate lead scoring, which is the best way to take advantage of prime sales opportunities. It’s also a good indicator that some prospects might need a little more nurturing before receiving any outreach from Sales.
If your emails include links to content hosted on your website or dedicated landing pages, you should also be incorporating engagements on the digital properties into your lead scoring algorithm. For example, visits to certain product or service pages might prompt higher lead scores than, say, a click on your About Us section. You should also exclude certain pages from scoring, such as your Terms and Conditions pages or your Careers pages. Additionally, you should be tracking engagements on social media and factoring those actions into your lead scoring model.
Continue to review and assess the efficacy of your lead scoring program at regular intervals. As companies grow and diversify, the way they interpret and prioritize leads is often subject to change. Work with your sales team to get a clear understanding of their version of a truly qualified lead and optimize and augment your scoring model to align with their processes and goals for better efficiency and success.
Learn More About Act-On’s Marketing Automation Platform
Lead nurturing is an essential part of the marketing funnel — perhaps the most important of all! But without the right platform and processes, it’s nearly impossible to get it right. Worse yet, if you try to do all this work manually, your team will spend an inordinate amount of time trying to manually segment your leads and execute your nurture campaigns with no guarantee of success or even that you’re sending to the right person or account.
Act-On’s marketing automation platform is designed to help marketing departments of all shapes, sizes, and skillsets attract, convert, and nurture highly qualified leads that they can either sell to directly via their website or pass to their sales team for more in-depth purchasing discussions. If you’d like to schedule a demo to get a better idea of the capabilities and benefits of Act-On, please click here, and we’ll be in touch!
If you’re interested in learning more about lead nurturing but not quite ready to speak with one of our marketing automation experts, please download our eBook, “How to Convert More Leads Into Prospects.” In it, you’ll find a comprehensive overview of lead nurturing benefits, best practices, and techniques that go beyond email marketing to include inbound, webinar, and social media tactics that help you maximize your marketing success.
5 Ways to Use Trigger Campaigns to Effectively Nurture Leads
3) Don’t Forget About Existing Customers
Every now and then we need to remind ourselves that keeping our existing customers happy should be our top priority. What’s more, people who have already bought from you are your best leads for potential upsells! And since you have all the information (and permission, in most cases) you need to market to these people, they’re perfect candidates for triggered email campaigns.
So when a customer makes a purchase, you should…
- Immediately send them a confirmation email thanking them for the sale and letting them know when and how they’ll be receiving their product.
- Once the product ships, send another email letting the customer know an estimated arrival time along with a tracking number.
- Finally, when you know the product has arrived, send an email asking for feedback or giving additional information on how to get the most value out of what your customer has just received.
All three of these follow-up emails present a great opportunity to sneak in additional product or content recommendations to continue the sales engagement.
4) Messaging Based on Lead Score
Every company scores and prioritizes their leads differently, but all organizations should have a threshold for determining a marketing qualified lead (MQL) and a protocol for passing those MQLs to Sales. Once a prospect meets that threshold (whether by opening an email, visiting a webpage, or clicking on a paid ad), you should send an automated trigger email to the prospect that invites them to take a more dedicated action like scheduling a call or booking a demo. By this point, the prospect should be extremely familiar with your brand, educated about your products and service lines, and even doing some competitive research, which means now is the time to take this relationship to the next level.
Conversely, MQLs often drop below your lead scoring threshold following a period of inactivity or by doing things you’ve determined a negative action, such as unsubscribing from your monthly newsletter. This is another good opportunity for you to attempt to re-engage with the prospect by offering highly personalized content and/or an exclusive offer. Once the prospect is requalified, be sure your sales team reaches out to strike while the iron is hot!
5) Instruct Your Customers on How to Use Your Products and Services
There’s nothing more frustrating than making an expensive purchase and then being completely lost about how to actually use the thing you just bought. Familiarity breeds a lot of assumptions, and just because you think something is easy to use or self-explanatory doesn’t mean your customers will feel the same. Thankfully, this provides a great opportunity to trigger a helpful email!
Whenever your customer makes a purchase and then receives their product or service, send a follow-up email providing clear and simple directions for how to implement or use it. You can then send a series of automated follow-up emails detailing some best practices and innovative techniques to help them get the most out of their purchase.
Again, this is highly personalized material that keeps prospects and customers engaged with your brand over time.
How to Use Your Microsoft Dynamics Sales Data to Power Marketing Automation Platform Campaigns
You probably already know that the purpose of a marketing automation system is to generate and feed as many qualified leads to your sales team as possible. But a marketer’s job doesn’t end there. In fact, if you’re not mining the sales data being generated by those leads, then you risk leaving additional sales and revenue on the table. To really benefit from all of that effort and expense put in to every marketing campaign, businesses need to leverage sales data from their Dynamics 365/CRM platform to power additional marketing automation campaigns. Knowing what you need to look at to really understand who your customers are and what they want can go a long way towards building out additional effective marketing campaigns. Let’s take a look at some of the best ways marketers can capitalize on the Dynamics data “goldmine” to reap the benefits.
Target existing customers
Your existing customers are obvious candidates for marketing reengagement. Once a purchase has been made, it makes sense to build out additional campaigns targeting these customers for special offers. Such campaigns can include “you might also like…” offers or upselling campaigns that entice customers with upgrades or service add-ons. But you don’t want to just hammer them with random emails pushing every product or service you sell. By taking a close look at the data in your Microsoft Dynamics environment and combining that data with you should be able to learn which customers are more likely to buy certain products or services based on past purchases, as well as the ideal time to engage them. You can then feed this information into your to generate timely campaigns that target the most likely buyers of additional products or services.
You can also use Microsoft Dynamics CRM data to identify past customers who have not purchased from you for a while. If you know how long your customers generally wait before buying a product again, and you probably do, you can deliver exclusive offers based on that specific timeline (for example 30, 60, or 90 days after last purchase). Your marketing automation platform can then be set to , re-engaging with them at just the right time.
Use Microsoft Dynamics sales data to personalize your marketing campaigns
More and more, your customers want to be treated like individuals, not just a group to be marketed to. According to a study conducted by , 86% of consumers now claim that personalization plays an important role when it comes to deciding on a purchase. And each individual customer you have has a unique preference when it comes to how they buy and how they want to communicate with your business. Your Microsoft Dynamics sales data can be a big help when it comes to personalizing the way you interact with these buyers.
You know more than just names and addresses of your past customers. Every interaction with them has been recorded in Dynamics, so use it to your advantage! Beyond simply addressing your customers by name (which you should, of course, be doing), you can incorporate all of the data you’ve collected on their buying habits and preferences to tailor additional marketing campaigns that show you know them as individuals. You can use your Microsoft Dynamics sales data to suggest ideas, package information just the way your individual customers like, and shape the way you communicate with them to align with their unique preferences. Not only will your sales increase, but the way your customers perceive you will improve as well.
Lookalike Audiences: Marketing to People Similar to Your Top Customers
If you are using your Microsoft Dynamics 365/CRM platform properly, then you should already know who your top customers are. Wouldn’t it be great to find more people just like them? You can, by creating your own “lookalike audience”. Simply put, a lookalike audience is a demographic group that resembles, in a variety of customizable ways, another demographic group. So by knowing your customers inside and out through careful analysis of your Microsoft Dynamics sales data, you can look for new customers with similar habits and preferences.
This tactic is especially effective on social media platforms, as many offer lookalike audience lists as a service. Once you’ve populated these lists based on data from Microsoft Dynamics, you can use your marketing automation platform to trigger personalized ads, emails, and other communications to potential customers who best resemble your best buyers. And depending on the size of your business, you can build out the campaigns to best suit your needs, modifying the size and look of these audiences to accommodate your specific needs and sales goals.
Connecting Microsoft Dynamics to your marketing automation platform
Connecting your Microsoft Dynamics environment to your marketing automation platform can open up a world of marketing opportunity, but it can be tricky to figure out where to start once your integration is complete. Learn what we recommend here, and be sure to see how the .
Three tips to make Dynamics CRM campaigns more fruitful
Your Dynamics CRM system has all the right tools and triggers to make effective campaigning as easy as “set it and forget it.”
Whether it’s a Quick Campaign to get that one offer in front of that one audience, or a more drawn-out, strategic nurture Campaign with multiple touch points, lead sources, and activities throughout, Dynamics 365 can take you from “nice to meet you” to to signed quote.
So why do some campaigns soar while others sink? After all, it’s the same tools, the same products and services, the same sales reps, etc.
It comes down to execution and content, and here are three simple tips to apply to every campaign you send through your CRM to help both improve engagement and speed throughput.
It’s not about you
“Since 19-blah-blah-blah, we have provided services, A, B, C to industries X, Y, and Z, enabling… [the putting to sleep of every single person with the misfortune of reading stuff like this].” How many email messages, sales proposals, or collateral have you seen that start like that?
Every message in every campaign sent through your CRM is not about you: it’s about the customer and the customer’s pain point.
Talk about the challenge you know they face (after all, you have a product and/or service that solves it!) instead of your corporate history. If you must talk about yourself in your campaign communications, use other customers to do so — case studies and other social proof say far more about your business than you alone ever could.
Automate every step you can
There are few things that sink a deal faster than miscommunication or missed communication. But if you automate your replies and check-ins, set reminders and tasks for reps, and take a “templated approach” to everything from prospecting emails to sales proposals, every communication can be timely, targeted, and far more effective.
For most B2B sales people, automating tasks and replies in the prospecting process is nothing all-too-new. But can prove challenging if you’re using CRM out of the box. However, a configure, price, quote solution (CPQ) makes sales proposal automation simple and seamless.
With as a single sign-on solution, every rep using your CRM can send more quotes more quickly, and ensure those quotes are professional in their look and feel, and have optimal product and pricing configurations embedded. CPQ is a no-brainer addition to your “automation equation.” (Contact me below for a CPQ demo.)
Nurture by nature
Drip campaigns are a thing of the past: it’s all about nurturing now. So if your sales reps are using your CRM system to send emails every 3, 10, and 15 days after a prospect downloads a whitepaper, for example, stop them right there.
Because emails and every customer contact should be based not just on a single action like a download, but on a comprehensive set of actions: web page visits and clicks, social media interactions, primary data from the prospect (e.g., job title, vertical market, etc.), and more.
And what you build is not a series of emails, but a nurture campaign that merges all customer behavior to create a comprehensive profile so that you go beyond, “Thanks for downloading: let’s set-up a demo,” to a series of responses and interactions unique to that prospect. While Dynamics CRM has some of these tools native, adding a full-fledged system makes nurture second nature.
Questions about improving your campaigning and sales closing process in Dynamics CRM? I would love to hear them!
5 Easy Tips to Create and Optimize Effective Email Campaigns
“What we’ve got here is a failure to communicate” – The Captain (Cool Hand Luke)
It pains me to say this, but a shocking amount of content marketers and demand generation specialists just don’t put a lot of effort into their automated email campaigns. I don’t know if it’s laziness, ignorance, or arrogance, but many of the emails I receive in my personal and work inboxes fall short on so many levels — and I’m not just talking about the occasional typo (although there are too many instances of grammatical errors and funky verbiage to mention).
No, the biggest and most frequent issues with these emails involve a fundamental ignorance of basic best practices — including unpredictable frequency, ineffective CTAs (or, worse yet, no CTAs at all), poor targeting and segmentation, excessively promotional language, and emails that aren’t optimized for mobile. Of course, there are a million mistakes marketers can make when ideating, writing, developing, executing, and optimizing their marketing automation campaigns, but I’d like to focus on the five I just mentioned for two reasons: 1) they’re all extremely common, and 2) they’re all extremely easy to fix.
Let’s take a closer look, shall we?
Unpredictable Frequency Toys with Email Recipients’ Expectations
There’s no right or wrong timeline for delivering the emails within your automated campaign; your scheduling will vary based on your industry, the products and/or services you provide, the audience you are trying to reach, and your typical sales cycle.
That said, you do have a responsibility to meet your email recipients’ expectations. So when you tell them you’ll be sending additional messaging in 3 days, send it in 3 days. Do what you say you will when you say you will to keep your audience engaged. If you don’t, you risk losing the opportunity to deliver key messaging that could’ve pushed them further through the marketing funnel.
Slapping Together Routine CTAs Is Lazy and Unproductive
You need to declare your desired outcomes before you even begin drafting the emails in your automated campaign, and each email should have a clear and engaging call-to-action that compels the recipient to take the desired action. This could be downloading an eBook or case study, viewing an on-demand webinar, or scheduling a demo of your products and services. Whatever you hope to achieve, you have to clearly state the desired action in a way that gets your audience excited to participate.
Obviously, your CTA needs to be reflective of the content of the email in which it’s included. While you should have a concrete understanding of the action you want your audience to take, the copy of the CTA will be informed by the copy of the email. According to research from Unbounce, 90% of online users who read a headline will also read a CTA, which means your big-ticket copy sections need to be on point.
Once the content of the email is in place, you should use the following tips to craft your CTA:
- Use verbs to stress action
- Inspire urgency by using words like “Now” or “Today”
- If the offer you’re prompting is free, be sure to let your audience know
- Whenever possible, use “me” rather than “you” to personalize the CTA
Whatever you do, make sure the CTA is in a prominent position in your email, and don’t hesitate to use the same or similar CTAs at multiple locations (e.g., above the fold and in your conclusion). Check the analytics around clickthrough rates (CTRs) regularly, and be sure to optimize based on the data at your disposal.
Know Who You’re Emailing and Why
My inbox is constantly flooded with promotional material that doesn’t speak to me directly as a consumer (e.g., Gap notifies me of sales for women’s shoes – not very helpful given my gender). And when this happens more than once from the same sender, I immediately unsubscribe. Life is cluttered and complicated enough without receiving irrelevant emails from ill-informed senders. Instead, senders should be focusing on delivering targeted, mutually beneficial emails.
Hopefully, you’ve created accurate, actionable personas based on the objective and empirical data at your disposal to get a clear picture of your ideal target audience and avoid the sort of mishaps I just mentioned. (If not, stop reading and click here.) Further, you should be tracking user behavior and how your audience typically interacts with your content to segment them into distinctive buckets. This is absolutely critical to your email marketing success because it helps inform the messaging you want to deliver and adds a level of personalization that humanizes your brand and makes your company more relatable.
Lastly, segmentation isn’t a set-it-and-forget it exercise. You should be reviewing the data consistently to continue improving your targeting. Even if you’re already receiving amazing results, you can always improve your outcomes by narrowing down the messaging based on more distinct segmentation. This is just one of the many benefits of marketing automation, which makes it easy to segment leads based on interests, behaviors, and needs — and enables marketers to personalize their efforts.
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Chill with the Sales-Heavy Messaging
No one wants to be sold — at least not explicitly. If someone has opted-in to receive your email campaign, you’ve clearly piqued their interest, so you should speak to them in a way that fosters that interest rather than manipulates it. Come from a helpful place of friendship instead of pressuring them to make financial decisions on the spot. Tell them more about the features and benefits of your products and services to get them excited about what you have to offer.
The best approach is to focus on your potential clients’ needs rather than your offerings. If you can clearly express how your products and services benefit the consumer, then you’ve subtly established its inherent value without cramming your company’s greatness down your audience’s throat. This approach is especially critical in your subject lines, which is where a significant percentage of your prospects are going to be won or lost. When your messaging is subtle, concise, and persuasive, there’s no need for the hard sell.
It’s Time We All Got On Board with Mobile Optimization
It’s a little awkward that I even have to mention this, but if your email campaigns aren’t optimized for mobile, it’s time to remedy that problem. It’s been almost four years since Google unleashed Mobilegeddon on her loyal subjects, but somehow thousands of marketers have yet to adjust to this brave new world — which means they’re missing out on some serious benefits. Just look at these stats:
- Nearly 40% of consumers utilize mobile devices while in-store shopping (Deloitte).
- The majority of emails are opened on mobile rather than desktop (eMailmonday).
- More than 50% of consumers who have a poor mobile experience are less likely to engage with the sender (Wow Local Marketing).
As you can see, optimizing your campaigns’ mobile responsivity is paramount in meeting your goals and driving leads through the marketing funnel. Naturally, the Act-On platform allows users to preview how their emails will look both on desktop and mobile, as well as the various internet service providers available.
Marketing Automation Provides Simple Solutions to Common Problems
Like I said, these are common problems, but they’re also easy to fix. And taking the time to resolve these issues will increase your deliverability and engagement metrics, which should likewise increase your conversion rates and the quality of the leads you’re passing to sales.
Marketing automation platforms, like Act-on, enable you to streamline the process of optimizing your email campaigns and improve your overall lead nurturing efforts. You can easily set up various email campaigns and gain insights to customer behavior that will allow you to continuously improve the success of your email marketing efforts.
How to Use CRM Campaigns to Prove Your Worth as a Marketer
I’ll let you in on a secret: in most organizations, marketing is a cost center. We’re the department of spending money. The department of pretty pictures and “branding” campaigns. The department where budget goes to disappear.
The good news is we can beat that perception by better putting to use the CRM Campaigns entity—all it takes is a little strategic thinking and some calculations of the data you already have available to you inside Dynamics. Here’s how to prove your worth as a marketer by using the Campaigns entity:
First, the Basics
“Campaign” can have a different meaning in the context of marketing than what you might find in the context of your CRM. In marketing, a campaign is a series of activities organized to work toward a particular goal, like sales. In the context of your CRM, a Campaign is a record that gives you a place to keep track of activities, costs, responses, and potential ROI of marketing spend.
There are occasions in CRM where a Campaign will not fit the standard definition of a marketing campaign—but we’ll get to that a bit later.
For the purpose of this post, we’ll focus on the CRM definition of Campaign. In Dynamics, this could be one of two types: either a Campaign or a Quick Campaign.
The differences are as follows:
A Quick Campaign is a great way to run a one-time offer or a flash sale, but if you’re looking for long-term tracking of a marketing activity (i.e., something you’d want to track an ROI to), your best bet is to create a Campaign.
With each new Campaign you build, you’ll want to add the following pieces of information (at a minimum):
- Name
- Actual Start/End
- Estimated Revenue
- Marketing Lists
- Campaign Activities (for tracking true cost)
- Offer
- Allocated Budget
We’ll get to gathering that information a bit later, but keep in mind these fields will help you keep track of your marketing successes in the long run. Plan on needing that information as you move forward.
The Architecture
Now that you’re oriented in the Campaigns Entity, you can create the architecture for your success. Dynamics has two layers of tracking available out of the box:
- Lead Source (an option set)
- Source Campaign (a pick list that pulls from new campaigns created in the Campaigns Entity)
Think of Lead Source as your big bucket—this is where you track the success of marketing activities as a category, like Events, Trade Shows, Paid Search, or Website Lead. This layer of tracking will help you understand what types of marketing events are performing well for your company.
Think of Source Campaign as your little bucket—here’s where you’re digging in at the campaign level to determine if a specific trade show or a specific paid search campaign was worth the investment. Your measurement here is the ROI on a specific campaign. This layer of reporting is great for you to know as you plan your next marketing budget, but it probably isn’t the type of report you would take to a financials meeting.
Once you select your Lead Source categories, be sure to map them to the appropriate Source Campaigns or campaign types. You’ll want to share this information with your sales team, like so:
Start Your Marketing Plan
You should have a marketing plan before you get your hands into the Campaigns entity. But getting this plan together will involve a dive into what’s happened historically on the Lead and Opportunity side of your CRM. In order to get the numbers you’ll need to prove your worth, you need to know the following:
- What’s your Lead to Opportunity conversion rate?
- What’s your Opportunity to Closed/Won rate?
- What’s your average deal size? Per product/service?
- What worked last year (e.g., where did your sales come from)?
- What do you want to try this year, and how much do you think it will cost?
This exercise does two things—first, you can set expectations for your leadership, yourself, and your sales team, and second, you can give yourself some baseline numbers to track your success. You might put these numbers into an Excel spreadsheet, where each Lead Source is a table and each row is a Source Campaign, like so:
In the Campaign entity, these numbers will be reflected in:
- Estimated Revenue
- Expected Response
- Budget ROI
This exercise is also useful for showing your sales team what success looks like from each event (how many Leads do we need? How many Opportunities do we need?) and for determining if the response you need to get your target ROI on an event is even possible.
Put Those Campaigns to Work!
Once you’ve built your budget, upload each row in your spreadsheet as a Source Campaign, along with the appropriate numbers from above. Keep in mind you will likely want to track additional sources that are not ROI-oriented, and that’s okay.
You can create perennial campaigns, like Referral, that don’t have an associated cost or projected ROI. All you’re trying to do here is figure out where your successes come from, and sometimes those successes aren’t from a true marketing campaign.
Proving Your Worth
You’ve got data—now you need reports. Think of your Little Buckets—your Source Campaigns—as what Marketing cares about, and your Lead Sources as what the executive team cares about, and create reports accordingly. You’ll need to know what’s working at the campaign level as you build out your budget proposal, but you can report to your executive team on the overall success of events as a strategy, or memberships as a strategy, and so on.
You can also use the numbers in your spreadsheet to set KPIs for your Marketing Department—how many total leads were driven in by Marketing last year, based on Source Campaign? That’s your starting point for creating a goal for this year. Similarly, how many Closed as Won Opportunities came from a Source Campaign that was a marketing activity? You now know what Marketing’s contribution to the sales pipeline is.
These are numbers that transform the Marketing Department from a cost center into a ROI-tracking machine—all the while proving the worth of the activities you’re running.
Want to see the calculations behind that sample budget spreadsheet? , and I will send a sample sheet your way.
Natalie Jackson is the Marketing Director at and a resident CRM + Marketing Automation Software geek.
‘Tis the Season: 11 Best Practices for Holiday Campaigns
Editor’s note: This article ‘Tis the Season: 11 Best Practices for Holiday Campaigns was originally published on CMO.com in October 2017.
Summer flew by in a blur, as it always seems to, and all of a sudden the beginning of the holiday shopping season is just weeks away. The time for digital marketers to prepare for crunch time is right now.
Digital holiday sales continue to grow in importance. Last year, Cyber Monday generated a record $ 3.45 billion in sales, just $ 110 million shy of traditional Black Friday sales in 2016. For the full season, online retailers took in a record $ 91 billion in sales, while mall traffic sank 12.3% in November and December of 2016.
Competition for the digitally acquired sale keeps increasing, and marketers really need to have their act together. The inbox is crowded real estate during the holiday season. Not only do messages have to pop to compete, they also have to be supported by the right sets of tools and infrastructure.
Keep in mind that in 2016, retailers sent 55% more emails on Black Friday, and 42% more on Cyber Monday than they did in 2015. Preparations need to be made for similar increases in growth this holiday season.
This means marketing automation and email providers have a lot to do to prepare for Black Friday, Cyber Monday, and the entire holiday shopping season.
Because email lists and the infrastructure to process digital campaigns are the backbones of successful digital holiday campaigns, think of your No. 1 priority as checking email lists, and checking them twice.
Our 11 tips for better holiday campaigns
Here are some tips for things you can start doing today to help assure success this holiday season.
- Email list hygiene: Up to 17% of Americans create a new email address every six months, and 30% of subscribers change email addresses annually. Chances are, one-third of email lists go bad every year. Determine an appropriate period since the last engagement for a specific account to be removed. Consider tests to clean lists of bounce-backs, assuring the highest possible percentage of successful deliveries.
- Start reactivation campaigns now: Identify opportunities to engage with accounts at risk of becoming dormant. Create offers and build personalized onboarding experiences for individual users.
- Complete customer profiles: The more data you have on individual customers, the better you can personalize offerings. Offering an incentive to customers to finish customer profiles, which may have been originally started some time ago, can lead to better data collection.
- Kick off nurturing campaigns: Advancing relationships with customers today will make them more receptive to holiday campaigns when released in November.
- Don’t just focus on Black Friday or Cyber Monday: The holiday season is longer than just a day or two. Start engaging as soon as possible and continue throughout the holiday season. Analyze clicks and offer additional incentives if sales aren’t made on the first attempt.
- Review current levels of engagement: Analyze this information to identify patterns related to active versus non-active accounts.
- Avoid data fatigue: How much email is too much email is a tricky question. Inevitably, some email accounts will go dormant or will ask to be removed. If email lists that you created have increasing numbers of dormant accounts, set policies to remove email addresses if reactivation plans aren’t successful.
- Create urgency: Uninteresting emails won’t be opened. Instill urgency in subject lines by offering a special discount that must be used within a certain period.
- Link email campaigns with other digital marketing activities: All digital marketing activities should be tracked and reinforced through each other. Banner ads, Adwords, and SEO strategies can support email campaigns and close additional sales.
- Ensure networks can handle increases in email traffic: Evaluate last year’s activity and plan for a 10% to 20% increase in volume, and be prepared to add more sending capacity if needed.
- Set realistic expectations and goals: Look at past performance and current resources to set realistic digital sales goals. If sales aren’t living up to initial projections early in the season, avoid the temptation to increase the volume of emails, which will lead to list fatigue.
Time is running out. But with the right planning, email campaigns can still deliver great results during the holiday crunch time when they matter most.
Remember to document best practices as well as what didn’t work and incorporate findings in future campaigns. Good luck!
6 Marketing Strategies to Improve Your Campaigns
You also may pivot your marketing messages and copy accordingly. Do you know how to talk to your customers?
3. Leverage trigger marketing.
So, you know who to talk to – and where. How about when?
If you can meet customers with your campaign at the right moment in the buyer’s journey – you’ll likely see more success. This is the concept of “right message at the right time.”
Trigger marketing – sending emails or campaigns at specific points in a customer’s engagement lifecycle – can significantly increase your chance of success.
To set up a trigger campaign, you first need to identify the key points in your customer’s journey, such as discovery, enrollment, first purchase, and/or renewal. Then add additional layers and next steps. For example, after discovery, assuming you can know the customer looked for you and visited your website, you can send them a follow-up: “See anything you like? Come back and visit us.” Or, if you have an existing customer who’s just made a purchase, follow up 30 days later with a reminder.
Marketing automation can help with that (more on that in a moment), but as you can also see, a robust CRM system can be an advantageous tool in trigger marketing.
4. Measure your marketing strategy results – track success.
So, let’s say you’ve conceived, created, and launched your campaign. You’re up and running. Great!
How’s it going? Do you know?
To find out, you must keep tabs on your efforts and formally track them.
This might look like a simple chart (think Excel tables) or a fancier dashboard. Whatever the case, you need to be sure to record what you’re doing – pull the info out of databases and your head – to document it for the future and share with colleagues.
I advise you to limit the pool of info you’re tracking to the few key barometers – KPIs – that really tell the story of your campaign. Endless data is available these days, but make sure you’re keeping an eye on the right data and checking it over and over (apples to apples) regularly.
And write it down! Our memories wane. If you don’t track and document information, it’s like that proverbial tree that falls in the forest … no one will know. (They can guess, but they won’t know for sure.)
5. Test to see how well your marketing campaign is performing.
Another key part of concepting and marketing is testing. Don’t forget to build tests into your campaign plans.
You need to measure what is working – and what isn’t.
Tests can be trying and tiring, but they’re also very valuable. There are myriad things you can evaluate:
You can test what channel works best. You can test how your web pages are doing, or see how your emails are performing. You can test time of day you publish content – or day of the week.
Tactically, this can look like A/B testing, such as trying different headlines, page designs, or subject lines if email is part of a campaign. It can also look like trying different or new channels or even targeting a new group of potential customers.
One word of caution: Try to control your tests so there aren’t too many things being evaluated at one time. Just like high school science class, you need to limit the number of constants and variables to glean true insight.
Also, testing isn’t a one-and-done affair. Testing should be done continuously. Don’t rest too long on those laurels. Keep testing – and tracking – to be sure you know what’s working.
6. Toss what doesn’t work.
Once you’ve started testing and tracking, it’s time to take stock. Step back and assess: Is it working? If so, bravo and great job. Time to rinse and repeat.
But, if not, you need to be confident enough to toss it. It’s OK to ditch things that aren’t working and try something new. Remember, you can learn from failure almost as much as – if not more than – success.
See how marketing automation can help.
The good news is Act-On software can help considerably when you’re applying these tactics.
We can help you with targeting, such as segmenting your lists. We can help you set up trigger-based emails. You can also use Act-On to tailor messaging once you’ve targeted your audience and determined the key triggers. And you can use our products to help you track success and report out to your colleagues or clients.
To learn more, contact us to speak to a rep.
Four Big Reasons Your Email Campaigns Are Being Ignored
When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company’s history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers’ viewing habits and determined that the show was likely to become a hit even before they purchased it.
The wisdom behind Netflix’s sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data.
Machine learning’s talents aren’t limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox? That’s machine learning. Or how you can casually converse with anthropomorphic voices coming from your smartphone? Also machine learning.
But these examples pale when compared to machine learning’s potential for remaking business. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence.
The untapped potential of machine learning
Here are some ways that machine learning could transform the core elements of the business ecosystem– and society:
Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self-learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.
Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.
Digital assistants and bots. Recent advances in machine learning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely. Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.
Plan for change
Although machine learning has already matured to the point where it should be a vital part of organizations’ strategic planning, several factors could limit its progress if leaders don’t plan carefully. These limitations include the quality of data, the abilities of human programmers, and cultural resistance to new ways of working with machines. However, the question is when, not if, today’s data analysis methods become quaint relics of earlier times. This is why organizations must begin experimenting with machine learning now and take the necessary steps to prepare for its widespread use over the coming years.
What is driving this inexorable march toward a world that was largely constrained to cheesy sci-fi novels just a few decades ago? Advances in artificial intelligence, of which machine learning is a subset, have a lot to do with it. AI is based on the idea that even if machines can’t (yet) duplicate the actual structures and thought patterns of the human brain itself, they can at least offer a rough approximation of important functions, such as learning, reasoning, and problem solving.
AI has been around since the 1950s, but it didn’t take off until the late 1990s, when Moore’s Law’s true exponential effects on computing power were realized, and researchers reined in their impulses to build a mechanized brain, focusing instead on using algorithms and machine learning to solve specific problems. Highly publicized machine-learning triumphs by IBM, such as Watson’s drubbing of human contestants on Jeopardy, captured the imagination of the public and business leaders.
Machine learning comes in several flavors, sometimes referred to as supervised learning (the algorithm is trained using examples where the input data and the correct answers are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning (the algorithm is rewarded or penalized for the actions it takes based on trial and error). In each case, the machine can learn from data – both structured (such as data in fields in a spreadsheet or database) and, increasingly, unstructured (such as e-mails or social media posts) – without explicitly being programmed to do so, absorbing new behaviors and functions over time.
Machines’ ability to learn puts them on an evolutionary path not unlike our own. They are gaining the ability to speak, listen, see, read, understand, and interact with ever-increasing sophistication. In just the last four years, the error rate in machine-learning–driven image recognition, for example, has fallen dramatically to near zero– practically to human performance levels.
Machine learning as collaborator
As machine-learning–based skills approach those of human beings, it’s tempting to view their evolution as a zero-sum competition with humans that we are destined to lose.
However, there is another view that says that automation will lead more to collaboration rather than outright replacement. Consulting firm McKinsey & Company argues that while 49% of jobs will be subject to some degree of automation, just 5% will be fully replaced anytime soon. In most cases, says McKinsey, automation will take over specific tasks rather than entire jobs.
McKinsey’s argument is compelling, at least when it comes to knowledge work, because it mirrors the way computing has evolved within the organization. Early mainframes were programmed to perform specific tasks, such as tallying up an organization’s daily receipts. When PCs were first introduced in the 1980s, they were dismissed by businesses as expensive typewriters until packaged spreadsheet software came along, allowing organizations to automate some of their manual accounting tasks at the individual employee level. Knowledge work would never be the same.
Today, most organizations have enterprise software that uses rules-based processing to automate many tasks in departments such as finance and human resources and in warehouses. Yet while the task-based automation of enterprise software has brought tremendous productivity improvements, the software could not learn and improve with experience as humans can.
Until now.
Thanks to advances in computer processing power, memory, storage, and data tools, machine learning can be integrated into the enterprise-software systems that form the heart of most organizational IT infrastructures. This means that the software, using the mastery that it develops in individual tasks, will be able to contribute increasing levels of performance and productivity to the organization over time, rather than merely offering a one-time boost, as most software packages do today.
The strength of machine-learning integration
The improvements the software brings to organizations will not be limited to individual tasks. One of the biggest strengths of enterprise software is its integration– the ability of individual applications to share information and be part of process workflows both within individual departments and across the organization. Integration allows organizations to experiment with new combinations of ever-more intelligent and versatile machine-learning applications and, where possible, let the machines learn how to improve the ways they work with each other and with their human colleagues. Together, these applications form the intelligent enterprise.
Just as individual applications will contribute more productivity to the organization as their embedded machine-learning abilities become more sophisticated, so too will the combinations of those applications evolve to bring more intelligence and flexibility to departmental and organizational processes over time.
Here are some concrete examples of how machine learning is creating value in organizations today:
Personalized customer service. Organizations can use machine-learning to improve customer service while lowering costs by combining natural-language processing, historical customer service data, and algorithms that continuously learn from interactions. Customers can ask the system questions and get accurate answers, lowering response times and allowing human customer service representatives to focus on higher-priority or more-complex interactions.
Financial-exception handling.
A machine-learning system can be trained to recognize payments that arrive without an order number and match them to invoices based on knowledge of customers’ order and payment histories. This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on more strategic tasks.
Improved hiring.
A machine-learning system can learn to pluck the most suitable job candidates from the thousands of résumés that organizations receive. It can also spot biased language in job descriptions that might discourage qualified people from applying and rescue other top candidates who fall through the cracks because they don’t fit with traditional hiring models.
Algorithmic security.
By building models based on historical transactions, social network information, and other external sources of data, machine-learning algorithms can use pattern recognition to automatically spot anomalies. This identification helps detect and prevent fraudulent transactions in real time, even for previously unknown types of fraud. And this type of algorithmic security is applicable to a wide range of other situations, including computer hacking and cybersecurity.
Image-based procurement. Instead of having to log into a procurement system and search manually, employees can simply use a smartphone app to snap a picture of the item they’re looking for– a particular brand and type of laptop, for example– and the system will use machine learning to hunt through its database to find a match or the nearest equivalent. It will then send a message to the employee, who can launch the ordering process with a single click.
Brand-exposure measurement. Brands spend billions on sponsorships, often without knowing exactly what they are getting for their money. A machine-learning application can sort through thousands of hours of sports video footage or track the action in real time, for example, to tell marketers how often their logo appears on screen, how large it is, how long it appears, and where it is located on the screen. Brands can then quantify their return on investment in the moment.
Contextual concierge.
Let’s say that your flight is suddenly delayed. A travel app on your smartphone can use context-sensitive machine learning to determine how this delay will affect your other travel plans and prompt you with rescheduling options.
Visual shelf management. Employees can take photos of shelves in a store aisle, kicking off a machine-learning process that automatically senses missing or improperly displayed items and prompts the store manager and the warehouse to fill the shelves correctly.
Manufacturing quality control. By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory.
Drone- and satellite-based inspection. A machine-learning system can sift through thousands of aerial images
of a pipeline, for example, and automatically spot areas that need maintenance or replacement.
Machine learning needs a platform
To be sure, organizations will gain tremendous benefits from individual machine-learning applications, even if they are never integrated into a larger whole. However, the benefits become much greater when these applications are on an integrated platform.
The business press has been discussing the power of platforms a lot lately, with iTunes being a well-known example. By creating a set of common software development tools that are available free to anyone who wants them, Apple has enabled developers to create thousands of applications for the iTunes App Store. Developers win because they can easily reach vast numbers of Apple device owners through iTunes. Apple wins because it takes a cut of the revenues for each app it makes available in the App Store.
Platforms are equally important to enterprises, not necessarily because of the profit motive (though some organizations are launching their own public, for-profit platforms similar to iTunes), but because having a platform gives them a base for quickly and cost-effectively combining different applications together, whether they are from different software vendors or are built in house.
No software vendor will ever be able to claim that it offers every machine-learning–enabled application that an organization needs out of the box. But vendors do offer platforms that organizations can use as bases for building out their entire machine-learning infrastructure.
The core of these machine-learning–enabled platforms is application programming interfaces (APIs). APIs are a kind of software version of those universal electric plug adapters that business travelers lug around with them so they can charge their electronic devices wherever they may be in the world. APIs allow software developers to plug into another software vendor’s applications without having to know anything about the complex code at the heart of those applications.
Another benefit of having a unified software platform is that organizations can use it to create a single point of access to data from across the organization. Data is the sole nutrient in a machine-learning diet. Algorithms need to binge on it constantly to lead a healthy and successful life. The larger and richer the data set, the more accurate the results. Having a single platform helps break down the data silos that exist across the organization so that organizations can make the most of machine-learning intelligence.
Organizations don’t need to go it alone
Inevitably, organizations will want to develop machine-learning–based applications that are not available in the marketplace. However, this does not mean that they need to create large internal machine-learning centers of expertise (although having some internal experts is recommended). Service providers can bring the expertise and perspective from within and across industries to help organizations focus on a small set of highly strategic processes that will benefit from machine learning.
The first step toward developing such applications is to determine where to apply machine learning. Organizations need to ensure that it erects barriers to entry against competitors or provides new ways of capturing and retaining customers by improving repurchase cycles or achieving new levels of win rates.
That means focusing investments on the machine-learning problems that will matter most to the industry’s basic competitive economics. Developing those engines will take considerable effort and time, so focusing the enterprise on those one or two projects that will really make a difference matters.
Here are five criteria to determine how to apply machine learning in a way that will create lasting differentiation.
1. The focus area as an appropriate candidate.
Not every facet of business will benefit from machine learning. The greatest potential is in automating high-volume tasks that have complex rules and large amounts of unstructured data.
Is your focus area big and complex enough for machine learning?
2. A clearly formulated issue. Machine learning works best on specific, well-defined tasks where the desired output and relevant inputs can be clearly stated: given X, predict Y. While it isn’t a magic bullet that will automatically help organizations learn from all the data in their enterprise, machine learning can be valuable in discovering correlations in large amounts of data that humans could never have deduced for themselves.
3. A sufficient quantity of examples to learn from. Machine learning requires a lot of data to be accurate. There must be enough examples for the machine to learn meaningful approximations of the decisions you want to make. This is discovered through experimentation.
4. Meaningful differences within the dataset. If the data you are trying to learn from does not contain meaningful differences, then the algorithm will fail at its mission. Let’s say that you are trying to identify different types of buyers. If the training data does not contain significant differences in buyer characteristics, the algorithm cannot give you useful results.
5. A clear definition of success. Machine learning is always evaluated by measures of performance on a specific task. Typically, the computer will try to optimize whatever performance measure is defined. Clear evaluation criteria for the algorithm are therefore critical. You also need to be certain that the evaluation criteria are actually helpful for solving your business problem.
Key evaluation criteria for machine learning
The human factor
Ultimately, the technical barriers to machine-learning adoption will be easier to solve than the human ones. Predictions of steep job losses due to automation are stoking fear and uncertainty about how these self-learning systems will impact our roles and our livelihoods.
These fears must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems into collaborative human-machine environments.
Indeed, self-learning machines have the potential to become valuable collaborators with humans, augmenting their skills and helping employees become more productive in their current jobs while freeing them from boring, repetitive tasks.
Experts also predict that machine learning will create new roles inside the organization. There is already a shortage of data analysts and those capable of developing the intricate algorithms that machine learning requires. Other new roles will become evident as machine learning integrates deeper into the organization – and not all roles will require a degree in computer science or math. For example, creative thinking, strategy development, quality management, and people development and coaching will be crucial skills in an AI-driven organization, according to a survey by consulting firm Accenture2.
What’s next
When machine learning matures to the point that it can handle unstructured data (still an issue today), when organizations openly share data, and when algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines, and software. That will enable highly context-sensitive insight at both the organizational and individual levels. We can only guess at the level of automation that will result, but the impact on business – and society – will be significant.
Already, commercial machine-learning applications based on these technologies are available, and more are being created all the time. That is why business leaders should engage now with trusted providers that can help them evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fueled insight and improvement. Together, they can address the cultural and change management challenges to take advantage of this new wave of business intelligence.
Download the white paper Why Machine Learning and Why Now?
Daniel Wellers is Digital Futures Lead, Thought Leadership Marketing, at SAP.
Jeff Woods is Vice President, Marketing Strategy and Head of Thought Leadership Marketing at SAP.
Dirk Jendroska is Head of Machine Learning Strategy and Operations, SAP Innovation Center Network, at SAP.
Christopher Koch is Director, Thought Leadership Marketing, at SAP.
Account-Based And Content Marketing Programs Are Not Campaigns
Some moments are so instantly, indelibly etched into pop culture that they shape the way we think for years to come. For virtual reality (VR), that moment may have been the scene in the 1999 blockbuster The Matrix when the Keanu Reeves character Neo learns that his entire life has been a computer-generated simulation so fully realized that he could have lived it out never knowing that he was actually an inert body in an isolation tank. Ever since, that has set the benchmark for VR: as a digital experience that seems completely, convincingly real.
Today, no one is going to be unaware, Matrix-like, that they’re wearing an Oculus Rift or a Google Cardboard headset, but the virtual worlds already available to us are catching up to what we’ve imagined they could be at a startling rate. It’s been hard to miss all the Pokémon Go players bumping into one another on the street as they chased animated characters rendered in augmented reality (AR), which overlays and even blends digital artifacts seamlessly with the actual environment around us.
For all the justifiable hype about the exploding consumer market for VR and, to a lesser extent, AR, there’s surprisingly little discussion of their latent business value—and that’s a blind spot that companies and CIOs can’t afford to have. It hasn’t been that long since consumer demand for the iPhone and iPad forced companies, grumbling all the way, into finding business cases for them.
If digitally enhanced reality generates even half as much consumer enthusiasm as smartphones and tablets, you can expect to see a new wave of consumerization of IT as employees who have embraced VR and AR at home insist on bringing it to the workplace. This wave of consumerization could have an even greater impact than the last one. Rather than risk being blindsided for a second time, organizations would be well advised to take a proactive approach and be ready with potential business uses for VR and AR technologies by the time they invade the enterprise.
They don’t have much time to get started.
The two technologies are already making inroads in fields as diverse as medicine, warehouse operations, and retail. And make no mistake: the possibilities are breathtaking. VR can bring human eyes to locations that are difficult, dangerous, or physically impossible for the human body, while AR can deliver vast amounts of contextual information and guidance at the precise time and place they’re needed.
As consumer adoption and acceptance drives down costs, enterprise use cases for VR and AR will blossom. In fact, these technologies could potentially revolutionize the way companies communicate, manage employees, and digitize and automate operations. Yet revolution is rarely bloodless. The impact will probably alter many aspects of the workplace that we currently take for granted, and we need to think through the implications of those changes.
VR and AR are related, but they’re not so much siblings as cousins. VR is immersive. It creates a fully realized digital environment that users experience through goggles or screens (and sometimes additional equipment that provides physical feedback) that make them feel like they’re surrounded by and interacting entirely within this created world.
AR, by contrast, is additive. It displays text or images in glasses, on a window or windshield, or inside a mirror, but the user is still aware of and interacting with reality. There is also an emerging hybrid called “mixed reality,” which is essentially AR with VR-quality digital elements, that superimposes holographic images on reality so convincingly that trying to touch them is the only way to be sure they aren’t actually there.
Although VR is a hot topic, especially in the consumer gaming world, AR has far more enterprise use cases, and several enterprise apps are already in production. In fact, industry analyst Digi-Capital forecasts that while VR companies will generate US$ 30 billion in revenue by 2020, AR companies will generate $ 120 billion, or four times as much.
Both numbers are enormous, especially given how new the VR/AR market is. As recently as 2014, it barely existed, and almost nothing available was appropriate for enterprise users. What’s more, the market is evolving so quickly that standards and industry leaders have yet to emerge. There’s no guarantee that early market entrants like Facebook’s Oculus Rift, Samsung’s Gear VR, and HTC’s Vive will continue to exist, never mind set enduring benchmarks.
Nonetheless, it’s already clear that these technologies will have a major impact on both internal and customer-facing business. They will make customer service more accurate, personalized, and relevant. They will reduce human risk and enhance public safety. They will streamline operations and smash physical boundaries. And that’s just the beginning.
Cleveland Clinic: Healing from the Next Room
Medicine is already testing the limits of learning with VR and AR.
The most potentially disruptive operational use of VR and AR could be in education and training. With VR, students can be immersed in any environment, from medieval architecture to molecular biology, in classroom groups or on demand, to better understand what they’re studying. And no industry is pursuing this with more enthusiasm than medicine. Even though Google Glass hasn’t been widely adopted elsewhere, for example, it’s been a big success story in the medical world.
Pamela Davis, MD, senior vice president for medical affairs at Case Western Reserve University in Cleveland, Ohio, is one of the leading proponents of medical education using VR and AR. She’s the dean of the university’s medical school, which is working with Cleveland Clinic to develop the Microsoft HoloLens “mixed reality” device for medical education and training, turning MRIs and other conventional 2D medical images into 3D images that can be projected at the site of a procedure for training and guidance during surgery. “As you push a catheter into the heart or place a deep brain stimulation electrode, you can see where you want to be and guide your actions by watching the hologram,” Davis explains.
The HoloLens can also be programmed as a “lead” device that transmits those images and live video to other “learner” devices, allowing the person wearing the lead device to provide oversight and input. This will enable a single doctor to demonstrate a delicate procedure up-close to multiple students at once, or do patient examinations remotely in an emergency or epidemic.
Davis herself was convinced of the technology’s broader potential during a demonstration in which she put on a learner HoloLens and rewired a light switch, something decidedly outside her expertise, under the guidance of an engineer wearing a lead HoloLens in the next room. In the near future, she predicts, it will help people perform surgery and other sensitive, detailed tasks not just from the next room, but from the next state or country.
Consumers are already getting used to 
thinking of VR and AR in the context of entertainment. Companies interested in the technologies should be thinking about how they might engage consumers as part of the buying experience.
Because the technologies deliver more information and a better shopping experience with less effort, e-commerce is going to give rise to v-commerce, where people research, interact with, and share products in VR and AR before they order them online or go to a store to make a purchase.
Online eyewear retailers already allow people to “try on” glasses virtually and share the images with friends to get their feedback, but that’s rudimentary compared to what’s emerging.
Mirrors as Personal Shoppers
Clothing stores from high-end boutiques to low-end fashion chains are experimenting with AR mirrors that take the shopper’s measurements and recommend outfits, showing what items look like without requiring the customer to undress.
Instant Designer Shows
Luxury design house Dior uses Oculus Rift VR goggles to let its well-heeled customers experience a runway show without flying to Paris.
Custom Shopping Malls
British designer Allison Crank has created an experimental VR shopping mall. As people walk through it, they encounter virtual people (and the occasional zoo animal) and shop in stores stocked only with items that users are most likely to buy, based on past purchase information and demographic data.
A New Perspective
IKEA’s AR application lets shoppers envisage a piece of furniture in the room they plan to use it in. They can look at products from the point of view of a specific height—useful for especially tall or short customers looking for comfortable furniture or for parents trying to design rooms that are safe for a toddler or a young child.
Painless Do-it-Yourself Instructions
Instead of forcing customers to puzzle over a diagram or watch an online video, companies will be able to offer customers detailed VR or AR demonstrations that show how to assemble and disassemble products for use, cleaning, and storage.
The customer-facing benefits of VR and AR are inarguably flashy, but it’s in internal business use that these technologies promise to shine brightest: boosting efficiency and productivity, eliminating previously unavoidable risks, and literally giving employers and managers new ways to look at information and operations. The following examples aren’t blue-sky cases; experts say they’re promising, realistic, and just around the corner.
Real-Time Guidance
A combination of AR glasses and audio essentially creates a user-specific, contextually relevant guidance system that confirms that wearers are in the right place, looking at the right thing, and taking the right action. This technology could benefit almost any employee who is not working at a desk: walking field service reps through repair procedures, guiding miners to the best escape route in an emergency, or optimizing home health aides’ driving routes and giving them up-to-date instructions and health data when they arrive at each patient’s home.
Linking to the Hidden
AR technology will be able to display any type of information the wearer needs to know. Linked to facial identification software, it could help police officers identify suspects or missing persons in real time. Used to visualize thermal gradients, chemical signatures, radioactivity, and other things that are invisible to the naked eye, it could help researchers refine their experiments or let insurance claims assessors spot arson. Similarly, VR will allow users to create and manipulate detailed three-dimensional models of everything from molecules to large machinery so that they can examine, explore, and change them.
Reducing the Human Risk
VR will allow users to perform high-risk jobs while reducing their need to be in harm’s way. The users will be able to operate equipment remotely while seeing exactly what they would if they were there, a use case that is ideal for industries like mining, firefighting, search and rescue, and toxic site cleanup. While VR won’t necessarily eliminate the need for humans to perform these high-risk jobs, it will improve their safety, and it will allow companies to pursue new opportunities in situations that remain too dangerous for humans.
Reducing the Commercial Risk
VR can also reduce an entirely different type of operational risk: that of introducing new products and services. Manufacturers can let designers or even customers “test” a product, gather their feedback, and tweak the design accordingly before the product ever goes into production. Indeed, auto manufacturer Ford has already created a VR Immersion Lab for its engineers, which, among other things, helped them redesign the interior of the 2015 Ford Mustang to make the dashboard and windshield wipers more user-friendly, according to Fortune. In addition to improving customer experience, this application of VR is likely to accelerate product development and shorten time to market.
Similarly, retailers can use VR to create and test branch or franchise location designs on the fly to optimize traffic flow, product display, the accessibility of products, and even decor. Instead of building models or concept stores, a designer will be able to create the store design with VR, do a virtual walkthrough with executives, and adjust it in real time until it achieves the desired effect.
Seeing in Tongues
At some point, we will see an AR app that can translate written language in near-real time, which will dramatically streamline global business communications. Mobile apps already exist to do this in certain languages, so it’s just a matter of time before we can slip on glasses that let us read menus, signs, agendas, and documents in our native tongue.
Decide with the Eye
More dramatically, AR project management software will be able to deliver real-time data at a literal glance. On a construction site, for example, simply scanning the area could trigger data about real-time costs, supply inventories, planned versus actual spending, employee and equipment scheduling, and more. By linking to construction workers’ own AR glasses that provide information about what to know and do at any given location and time, managers could also evaluate and adjust workloads.
Squeeze Distance
Farther in the future, VR and AR will create true telepresence, enhancing collaboration and potentially replacing in-person meetings. Users could transmit AR holograms of themselves to someone else’s office, allowing them to be seen as if they were in the room. We could have VR workspaces with high-fidelity avatars that transmit characteristic facial expressions and gestures. Companies could show off a virtual product in a virtual room with virtual coworkers, on demand.
Reduce Carbon Footprint
If nothing else, true telepresence could practically eliminate business travel costs. More critically, though, in an era of rising temperatures and shrinking resources, the ability to create and view virtual people and objects rather than manufacturing and transporting physical artifacts also conserves materials and reduces the use of fossil fuel.
The strength of digitally enhanced reality—and AR in particular—is its ability to determine a user’s context and deliver relevant information accordingly. This makes it valuable for monitoring and managing employee behavior and performance. Employees could, for example, use the location and time data recorded by AR glasses to prove that they were (or weren’t) in a particular place at a particular time. The same glasses could provide them with heads-up guided navigation, alert employers that they’re due for a legally mandated break, verify that they completed an assigned task, and confirm hours worked without requiring them to fill out a timesheet.
However, even as these capabilities improve data governance and help manage productivity, they also raise critical issues of privacy and autonomy (see The Norms of Virtual Behavior). If you’re an employee using VR or AR technology, and if your company is leveraging it to monitor your performance, who owns that information? Who’s allowed to use it, and for what purposes? These are still open legal questions for these technologies.
Another unsettled—and unsettling—question is how far employers can use these technologies to direct employees’ work. While employers have the right to tell employees how to do their jobs, autonomy is a key component of workplace satisfaction. The extent to which employees are required to let a pair of AR glasses govern their actions could have a direct impact on hiring and retention.
Finally, these technologies could be one more step toward greater automation. A warehouse-picking AR application that guides pickers to the appropriate product faster makes them more productive and saves them from having to memorize hundreds or even thousands of SKUs. But the same technology that can guide a person will also be able to guide a semiautonomous robot.
The Norms of Virtual Behavior
VR and AR could disrupt our social norms and take identity hacking to a new level.
The future of AR and VR isn’t without its hazards. We’ve all witnessed how distracting and even dangerous smartphones can be, but at least people have to pull a phone out of a pocket before getting lost in the screen. What happens when the distraction is sitting on their faces?
This technology is going to affect how we interact, both in the workplace and out of it. The annoyance verging on rage that met the first people wearing Google Glass devices in public proves that we’re going to need to evolve new social norms. We’ll need to signal how engaged we are with what’s right in front of us when we’re wearing AR glasses, what we’re doing with the glasses while we interact, or whether we’re paying attention at all.
More sinister possibilities will present themselves down the line. How do you protect sensitive data from being accessed by unauthorized or “shadow” VR/AR devices? How do you prove you’re the one operating your avatar in a virtual meeting? How do you know that the person across from you is who they say they are and not a competitor or industrial spy who’s stolen a trusted avatar? How do you keep someone from hacking your VR or AR equipment to send you faulty data, flood your field of vision with disturbing images, or even direct you into physical danger?
As the technology gets more sophisticated, VR and AR vendors will have to start addressing these issues.
To realize the full business value of VR and AR, companies will need to tackle certain technical challenges. To be precise, they’ll have to wait for the vendors to take them on, because the market is still so new that standards and practices are far from mature.
For one thing, successful implementation requires devices (smartphones, tablets, and glasses, for now) that are capable of delivering, augmenting, and overlaying information in a meaningful way. Only in the last year or so has the available hardware progressed beyond problems like overheating with demand, too-small screens, low-resolution cameras, insufficient memory, and underpowered batteries. While hardware is improving, so many vendors have emerged that companies have a hard time choosing among their many options.
The proliferation of devices has also increased software complexity. For enterprise VR and AR to take off, vendors need to create software that can run on the maximum number of devices with minimal modifications. Otherwise, companies are limited to software based on what it’s capable of doing on their hardware of choice, rather than software that meets their company’s needs.
The lack of standards only adds to the confusion. Porting data to VR or AR systems is different from mobilizing front-end or even back-end systems, because it requires users to enter, display, and interact with data in new ways. For devices like AR glasses that don’t use a keyboard or touch screen, vendors must determine how to enter data (voice recognition? eye tracking? image recognition?), how to display it legibly in any given environment, and whether to develop their own user interface tools or work with a third party.
Finally, delivering convincing digital enhancements to reality demands such vast amounts of data that many networks simply can’t accommodate it. Much as videoconferencing didn’t truly take off until high-speed broadband became widely available, VR and AR adoption will lag until a zero-latency infrastructure exists to
support them.
For all that VR and AR solutions have improved dramatically in a short time, they’re still primarily supplemental to existing systems, and not just because the software is still evolving. Wearables still have such limited processing power, memory, and battery life that they can handle only a small amount of information. That said, hardware is catching up quickly (see The Supporting Cast).
The Supporting Cast
VR and AR would still be science fiction if it weren’t for these supporting technologies.
The latest developments in VR and AR technologies wouldn’t be possible without other breakthroughs that bring things once considered science fiction squarely into the realm of science fact:
- Advanced semiconductor designs pack more processing power into less space.
- Microdisplays fit more information onto smaller screens.
- New power storage technologies extend battery life while shrinking battery size.
- Development tools for low-latency, high-resolution image rendering and improved 3D-graphics displays make digital artifacts more realistic and detailed.
- Omnidirectional cameras that can record in 360 degrees simultaneously create fully immersive environments.
- Plummeting prices for accelerometers lower the cost of VR devices.
Companies in the emerging VR/AR industry are encouraging the makers of smartglasses and safety glasses to work together to create ergonomic smartglasses that deliver information in a nondistracting way and that are also comfortable to wear for an eight-hour shift.
The argument in favor of VR and AR for business is so powerful that once vendors solve the obvious hardware problems, experts predict that existing enterprise mobile apps will quickly start to include VR or AR components, while new apps will emerge to satisfy as yet unmet needs.
In other words, it’s time to start thinking about how your company might put these technologies to use—and how to do so in a way that minimizes concerns about data privacy, corporate security, and employee comfort. Because digitally enhanced reality is coming tomorrow, so business needs to start planning for it today. D!
Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.