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Tag Archives: Behavior

B2B Buyer Behavior Is Changing: Making Experience The Top Objective

March 5, 2020 BI News and Info

In this new decade, businesses continue to navigate digital transformation in hopes of finding new ways to grow their brand while remaining cost-efficient. To support these initiatives, more companies are turning to digital channels when purchasing software. This allows for a much quicker process from the discovery and trial phase to purchase and use. With the pace of business continuously speeding up, digital buying is attracting more attention and starting to take a serious lead.

A shift in buyer behavior like this brings a lot of change to how things are bought and sold in the B2B space. To understand key transformations in buying patterns and digital purchases, we partnered last year with Futurum Research to source and publish the 2019 B2B Digital Buyers’ Journey Report. The findings are key to helping better inform B2B sellers’ strategies for the years ahead. Some interesting trends show businesses are no longer sticking to old timetables for buying software and are de-prioritizing legacy relationships when deciding who to buy from.

Understanding key shifts in today’s B2B buying process

According to the research, nearly 80% of businesses no longer rely on calendarized buy cycles to purchase software. In fact, more than half say they are ready to make a purchase anytime a solution is needed to keep the business moving forward, rather than waiting for budget approvals or biding time until upgrade and purchase periods come around. Similarly, the data also uncovered another major shift: fewer than one in five organizations still consider legacy relationships a factor when choosing enterprise software solutions to purchase digitally.

As a result of these changing habits, we are seeing the digital experience firmly take its place at center stage. The most simple, efficient, and highly personalized experiences are the ones that keep existing customers coming back and attracting new ones. Personalization combined with one-to-one digital engagement between a brand and each of its customers aren’t new concepts. But as this paradigm shift in buyer behavior takes a deeper hold on the B2B world, it’s important that B2B brands get it right by embracing these techniques.

Keeping up with the speed and complexity of modern business

The conversation for some time has centered around access to customer data and the ability to glean real-time insights from that data in order to make it actionable. However, the process of personalization and engagement is becoming increasingly more complex. The speed at which business is done now has a lot to do with this and puts even more pressure on brands to provide unique customer experiences at each new interaction and purchase stage.

On top of this, B2B buyers have specific demands when deciding what to purchase. Product trials are still very important along the digital buying journey, with 90% of organizations considering them to be a key factor in the decision-making process. In addition, 85% rated one-on-one online product demos and video product demos as highly important to their buying journey, further showing the important role of trials in determining how and what is purchased.

Embarking on the digital buying journey

Digital channels allow businesses to move far more quickly than they can with traditional sales processes. While many B2B purchases still require a higher level of touch with a salesperson or team, businesses are increasingly showing that they also want alternative options for faster, lower-touch ways to buy. It’s clear an omnichannel approach to B2B buying, one that provides digital options as a faster way to purchase when needed, is essential to move business forward.

The pace at which change and technological advancement are occurring across industries is a major driving force in the behavior shifts happening in the B2B buying landscape. Purchases are now based on needs of the moment, and brands are choosing to engage with the solution providers that give them the best, most personalized experience, tailored to their unique business goals.

As more brands look to source and purchase the solutions they need via online channels, B2B businesses need to let go of traditional sales methods. The path forward will require organizations to leave behind the comfort of being a legacy provider in favor of building new relationships with customers online – ones that map to their growing digital needs in order to keep business moving.

This article was originally published on CustomerThink.com and is republished by permission.

Learn more about the best ways to create omnichannel customer experiences.

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Digitalist Magazine

How Suppliers And Supply Chains Change Consumers’ Buying Behavior

November 7, 2019 BI News and Info

We are all consumers and we live in a world of global supply chains, global production networks with increasingly complex structures as well as cross-border deliveries. Products are manufactured in many countries and travel almost around the globe, before they end up on the shelf. The management of the entire supply chain across multiple supplier tiers is even more sophisticated, if we think about just-in-time or low-cost country sourcing. Over the last years, we have seen two dominating trends upcoming, which are highly relevant for managers of global supply chain networks: geopolitical instability and alignment with sustainability initiatives such as Fridays for Futures.

First, based on the recent political developments with tariffs, embargos and trade wars, companies are facing a much more unsecure and unstable environment. Where in the past, we had long-standing trade-relationships across nations and a stable environment for companies, this situation is completely altered with new tariffs policies in the United States, China, the European Union, or with the upcoming Brexit. Therefore, companies need to be more flexible and agile – not only from a technology perspective with the digital transformation theme, but also from an organizational DNA perspective to react flexibly to upcoming economical and geo-political challenges.

There are two key questions a leader can challenge their organization to ease the impacts of geopolitical instability:

How flexible can a subcontractor and supplier be changed in a company’s just-in-time manufacturing, if we follow a single sourcing strategy?
How flexible is our manufacturing network and how fast can we deploy a new site respectively the capacity outside the affected country?

How these questions are addressed through a series of people, process, and technology transformations can go a long way to mitigate geopolitical instability.

The second dominating trend is upcoming with the Friday for Futures movement. Especially as our younger generation is thinking and acting more sustainable and is asking for more responsible, ecological behaviors such as reducing carbon footprint, plastics and climate change. This level of social thinking changes the buying behavior from end-consumers and is disrupting industries.

According to a recent study, 47% of people do not trust that brands are producing goods fairly and sustainably. The same study reveals that 48% of people would be willing to pay more for products from brands that are responsible with their supply chain management. In another study, researchers at the MIT Sloan School of Management found that consumers may be willing to pay 2% to 10% more for products from companies that provide greater supply chain transparency.

The value proposition to consumers is clearly in favor of supply chain transparency. End-consumers want to know where products are manufactured and whether companies comply with a sustainable behavior. From a marketing perspective, we already see companies publishing information such as “Designed in California – Manufactured in China.” The benefit from a company’s perspective is increased brand loyalty, where 65% of consumers surveyed agreed that they would be more loyal to brands adhering to the United Nations’ Sustainable Development Goals.

Conclusion

Not only have scholarsspoken, but consumers also offer up empirical evidence that supply chain transparency and social responsibility can drive market share, margins, and brand loyalty. To truly impact these three metrics, leaders need to perform more than cosmetic changes to their supply chains. They are required to make the transformative adjustments within their organizations and extend such transformations to multiple tiers with their supply and demand chain.

Are you interested in analyzing your own organization’s social purpose performance? Participate in our purpose survey to receive a personalized report that provides visibility into your current performance, and how you compare vis-a-vis peers.

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Digitalist Magazine

Change to Summarized Data Export behavior with Build permission

August 22, 2019 Self-Service BI

As we rolled out the preview of Shared Datasets and the new Build permission, we received feedback that our limitations on export of summarized data from a visual are overly restrictive.

Today a user with Read permission can see a visual’s data in two ways. First, they can see it rendered in the visual. Second, they can use the See Data feature to view a grid of the data used to render the visual. The grid contains only the summarized data.

As such the user has access to the summarized data for a visual already. Before the recent permission changes to introduce the Build permission, users with the Read permission were also able to export the summarized data from the visual to a CSV file. With the changes after Build was introduced, export summarized data required Build permission even though users could still access the same data as described above.

We are adjusting our approach based on your feedback. Specifically, we will allow users with the Read permission to export summarized data from a visual. This change will be rolling out over the next week.

Exporting the underlying data for a visual will continue to require Build permission. The underlying data is the detailed rows that were rolled up to show in the visual. This ensures that consumers do not export more data than the report is showing them.

For organizations needing a truly locked down data export experience, we offer several existing controls:

The report author can fully prevent any data export, can allow exporting only summarized data, or also allow export underlying data for users with the Build permission.
The Power BI admin can control which users can export data across all content in Power BI. This can be used to lock down all data export experiences.

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Microsoft Power BI Blog | Microsoft Power BI

Effective Email Marketing Uses Data, Behavior, and Customer Personas

April 8, 2019 CRM News and Info

Most of us know that email is one of the most effective tools marketers have to nurture their audience, gain new leads, and drive conversions. Getting the most out of email, however, requires the right email marketing strategy, and developing that strategy involves more work than just setting business goals and determining topics.

The email marketing approach that works best for your organization will vary based on numerous factors that influence audience engagement and deliverability. You also have to go beyond a one-size-fits-all approach to make sure you’re addressing the various people involved in the buying process. Keeping these multiple personas in mind is crucial to helping you tailor your efforts to meet their specific needs while improving the chances of your emails getting delivered, opened, and read.

Since deliverability best practices and audience preference are fluid, finding the right path toward email marketing success can be daunting. Today, we are on a mission to remove the mystery involved in creating an effective email marketing strategy and equip you with the tools and knowledge you need to figure out a recipe that gets results.

Keep reading to learn how to use data, behavior, and customer personas to develop an email marketing strategy that improves inbox placement and captures your audiences’ attention.

Segment Your Audience to Improve Personalization and Deliver Relevant Content

Personalizing your marketing efforts is necessary if you want to maximize engagement and, in turn, improve your inbox placement. You can’t expect all of your contacts to be at the same decision stage or share similar pain points, and sending an email that doesn’t resonate can result in poor engagement or, worse yet, get sent to the spam folder. Delivering content that matches your recipient’s needs and preferences will prove your business’ value and help them progress from one stage in the buyer journey to the next.

Segmenting your contacts appropriately is the first step toward delivering a more personalized customer experience. Although segmentation has long been regarded as a huge undertaking by marketers, grouping your contacts into lists doesn’t have to be laborious and time-consuming. The right marketing automation tool (such as Act-On), removes much of the work involved by enabling you to automatically segment your contacts based on their behavior as well as the information you’ve gathered through form fills.

While there are dozens of ways to segment your contacts, a good place to start is by their position at their organization and stage in the sales cycle. Once you begin to engage with your contacts and learn more about their specific preferences, you can use this information to segment them into a new list and enter them into a new automated email campaign.

Look at Your Data to See What’s Working (and What’s Not)

Accurate and thorough customer personas provide a good starting point toward delivering personalized emails that resonate with your audience and motivate engagement. Data, however, enables you to go the extra mile and get even more granular with your efforts by providing visibility and insight into whether your subject lines, CTAs, graphics, and other email elements resonate with your recipients.

You can begin collecting data and gain even more detailed insights into your customers’ preferences by conducting A/B testing for every email you send. Send at least two versions of each email to measure which components inspire your audience to open and click through. Once enough time has elapsed and you’ve gathered sufficient data, use these insights to revise your emails to highlight the components that seem to be driving engagement for your audience.

There are plenty of analytics tools available, so you don’t have to be a data scientist to thoroughly measure the performance of your emails. If you’re using Act-On, Data Studio allows you to view delivery, bounce, and open rates and click maps so you can gain a more detailed sense of how your content is influencing the deliverability and engagement of your emails.

Avoid Email Fatigue By Monitoring Audience Behavior

Email fatigue is a real thing, and (in addition to negatively impacting engagement) it can result in an uptick of opt-outs and spam complaints, which are the two fastest and most common routes to a damaged email reputation. Monitoring audience behavior for spikes in spam complaints, emails, and lack of engagement can help you adjust your timing and cadence to avoid bombarding your audience with unwanted emails in the future.

In addition to considering audience preferences and engagement, you can avoid causing email fatigue by limiting your sending during certain times of the year when your audience is likely to receive an influx of emails, such as the holiday season. Another good rule of thumb to prevent email fatigue is to avoid over-sending in the first place. Even if your contacts sign up to your email list and choose to receive communications from you, they might change their mind if you start flooding their inbox. Therefore, it’s best practice to use forms to learn more about what and how often they want to hear from you.

Continuously Revisit Your Strategy for Best Results

As with all your marketing efforts, your email marketing strategy should never remain stagnant. Your customers and best practices for deliverability are constantly changing, so it’s best to periodically revisit your email marketing strategy to reflect these changes for optimal results. This will help you stay ahead of the competition and ensure that you’re always getting the most bang for your buck when it comes to your email marketing efforts.

If you’d like to learn more about how you can use data, behavior, and personas to develop an effective email marketing strategy, check out our eBook Deliverability 101: Your Guide to Developing an Email Strategy That Improves Deliverability and Drives Results (also linked below).

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Act-On Blog

The Social Behavior Of Honeybees Applied To IoT Systems

December 5, 2018 SAP

The Internet of Things (IoT) has been used to great effect to enhance beekeeping. As beekeepers with day jobs in IoT and mobile solutions, we are always keen to combine our interests. Significant beekeeping benefits from IoT technologies lie in monitoring beehives remotely, thereby reducing the time spent inspecting bees and decreasing the bees’ stress levels. However, while IoT can improve the way we care for bees, the bees have much to teach us about how we can improve IoT.

As we work to build connected cities, elevating the interplay of technology and the way we live, it’s worth considering how nature has long been doing something similar by enabling each bee to “connect” with the rest of the colony to live together prosperously.

Bees as superorganisms: Systems of systems

Honeybees (apis mellifera) are fascinating creatures. As beekeepers, we are continuously amazed at how a honeybee colony behaves as a single social structure. It can be argued that a honeybee colony is actually a superorganism. An organism might appear to be a single animal, but it is actually a huge collection of different types (or castes) of cells. The key definition of a superorganism would be a social unit of eusocial animals, within which the division of labor is highly specialized and where individuals are not able to survive by themselves for extended periods. The definition certainly describes a honeybee colony.

A network of IoT devices behaves very much like a bee colony. In fact, we can learn a great deal from nature’s superorganisms. Bee colonies can teach us how to improve our design and management of IoT networks. The beehive could be considered a truly connected city. Tens of thousands of bees pack into well under a square meter, all functioning together to protect and clean the hive, gather and store food, and rear the young. The living density and sheer focus on the survival of the hive – at the expense of the individual – may not be something we want to replicate. The level of communication across the hive is worthy of admiration.

In an organism, while the cells have the same DNA, they are also very specialized. They perform different functions. A social insect such as apis mellifera (the honeybee) meets the superorganism criteria quite well. Individual organisms have hormones to organize body processes. Bees use pheromones that serve the same role as hormones. Pheromones are chemicals that transmit information from one whole organism to another. In the social structure of a honeybee colony, pheromones are used throughout the lifecycle of the bee to attract drones to a virgin queen and delineate each stage of the brood development. Pheremones also function to distinguish among the different castes of bees – worker brood from drone and queen – as well as to stimulate activity in a hive, acting as a marker to help bees identify home. Bees leverage pheromones to distribute a message rapidly, alerting others to an attack, which turns thousands of bees from passive into aggressive. All beekeepers can attest to the speed and effectiveness of this messaging.

Pheremones and smart cities

In Chapter 5 of the book: Neurobiology of Chemical Communication, the authors note, “Honeybee pheromones represent one of the most advanced ways of communications among social insects. For more detailed information in honeybee pheromones, refer to the link above, as the book is a fascinating read.

In many ways, a collection of interconnected IoT sensors in a smart city is like a honeybee colony – a superorganism – with many different individual types (or castes) of sensors, all communicating among themselves using various protocols (pheromones) as a community (hive) to achieve a set of goals.

So, what can we learn from bees, and how can we apply those highly-effective communication strategies to IoT?

Simplicity, security, and smart communication

Simplicity: Bees have a very clear vocabulary. Dance is an integrated form of communication that bees use. Location is communicated in terms of bearing and distance. Bees will travel up to four miles. The terrain they cover over this distance may well be complex and varied – especially for a small bee. Only bearing and distance are relevant, however, and these are all that’s communicated by the angle of the bee’s waggle dance and vibrations.

The waggle dance is a simple but highly efficient form of communication that bees use to tell others in the hive where the best source of food is. A very simple approach is taken to weighing the different reports of food. The more bees making the dance for a feed location, the greater the assumed supply.

Security: Pheromones are key to a bee’s secure communication. The precise balance of encoded chemicals validates vital intra- and extra-hive communications. For example, the queen will have a very precise chemical signal. A hive will know if its queen is present on this basis, and they can spot imposters rapidly.

Smart communication: With tens of thousands of bees in a hive, multiple food sources, potential threats, and a lifecycle from egg to hatchling that is at times continuous, the potential for miscommunication may be great, but actual misunderstandings are rare. Key to achieving this accuracy of communicating is that bees only communicate what is necessary. They sort out and forward along only that which is relevant to the rest of the hive. Each bee is focused on its own task. They perform it diligently, ensuring the overall wellbeing of the hive. For example, honeybees are economically savvy when they forage. Bee flight is ultimately costly to the bee, and foragers will not collect at long distances unnecessarily. Therefore, when bees communicate the location of good forage, distance is a major factor.

What IoT can glean from honeybees

By keeping connectivity simple, secure, and smart we, too, can make a success of connecting the world around us. We can avoid the trap of making life unnecessarily complex.

It’s also worth noting that while hives have a queen, hives are in fact democratic. It’s the workers who decide when it’s time for a new queen. They have no compunction about replacing the queen. It took a long time for humans to replicate that model of government. It looks like honeybees have been ahead of us all along.

If you’re working with “The Internet Of Things: Get Ready For Big Changes Ahead In 2019.”

This article originally appeared on IoT for All.

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Digitalist Magazine

Nuance Prediction Service enables brands to forecast customer behavior

September 19, 2018 Big Data

Predictions are artificial intelligence’s (AI) forte, you might say. Some of the most accurate machine learning models draw on hundreds of thousands (or even millions) of data points to surface patterns that would otherwise go undetected. Adobe is using AI to predict when to send marketing emails. Gogo is tapping it to predict equipment failures. And Nuance claims it can be used to deliver tailored, proactive service to customers.

Nuance this week announced Nuance Prediction Service (NPS), a new tool within its Customer Engagement Platform that enables brands to forecast client behavior and respond in an automated, contextualized fashion. It aggregates data from transactions “across all channels” — including the web, text messages, apps, internet of things devices, and more — to fuel its prediction models, over time refining them with “constant analysis.”

“Large organizations today are fielding billions of customer interactions each year, and being able to anticipate and resolve customer needs in advance offers not only enormous potential for savings and operating efficiencies, but perhaps more critical, advances the customer experience,” Robert Weideman, executive vice president and general manager of Nuance’s Enterprise Division, said. “Prediction Service fuels the kind of highly personalized, omnichannel strategy that so many organizations are trying to achieve to engage their customers intelligently, while providing self-service that makes tremendous impact on efficiencies.”

Nuance claims NPS can improve containment rates, routing accuracy, conversion rates, and other key performance indicators.

It brings to mind South Africa-based startup Xineoh, which this summer unveiled its own AI-powered platform for predicting customer behavior. It says its tech can, for example, match marketers with people most likely to buy a given product, or identify customers on the cusp of canceling a subscription.

NPS appears to be slightly more limited in scope, but Nuance has the advantage of scale. The Burlington, Massachusetts-based company, which was founded in 1992, says it automates an estimated 16 billion customer interactions a year across voice, text, and digital channels. Moreover, more than 6,500 companies already leverage Nuance’s solutions for customer engagement, including Coca-Cola, Delta Airlines, FedEx, The Commonwealth Bank of Australia, Swedbank, TalkTalk, and USAA.

Nuance is also no newcomer to AI. The firm’s natural language processing technology once powered Apple’s Siri, and in the not-too-distant past, it’s teamed up with tech giants like Nvidia to power AI-driven radiology and conversational virtual assistant platforms.

“We are looking forward to bringing this to our customers and continuing to push the envelope on our AI-powered approach to engagement,” Weideman said.

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Big Data – VentureBeat

Confusing behavior when passing a variable vs. inlining a function call

August 2, 2018 BI News and Info

Newbie question here. Consider the following two functions:

f1[n_] := (
  q = {0, 0};
  Do[q[[RandomInteger[{1, 2}]]] += 1, n];
  Return[q]
  )

f2[n_] := (
  q = {0, 0};
  Do[k = RandomInteger[{1, 2}]; q[[k]] += 1, n];
  Return[q]
  )

Both seem to be doing the same thing: create a list of zeros, increment a random element $ n$ times and return the list. The difference is that the first version “inlines” RandomInteger call into the indexing, while the second defines an intermediate variable k.

The function f2 works as expected, while f1 does not. For example, f1 sometimes returns lists for which sum of elements is not equal to the input n, which seems very strange.

In[357]:= f1[10]

Out[357]= {6, 7}

Can someone point out why f1 and f2 are treated differently?

2 Answers

Mathematica is an expression rewriting language. When it evaluates:

q[[RandomInteger[{1, 2}]]] += 1

It first rewrites it as:

q[[RandomInteger[{1, 2}]]] = q[[RandomInteger[{1, 2}]]] + 1

The result is that RandomInteger[{1, 2}] gets rewritten twice, possibly as different random integers.

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Recent Questions – Mathematica Stack Exchange

Pet Family Behavior

April 27, 2018 Humor

Dog and birds at their best.

“We’re just one big happy family… Well, sort of.”
Image courtesy of https://imgur.com/gallery/CRno5JJ.

Behavior of Possible Zero Q

June 14, 2017 BI News and Info

I was slightly surprised by the following result:

In:= PossibleZeroQ[Gamma[a + b]/Gamma[a] - Pochhammer[a, b]]

Out:= False

versus

In:= PossibleZeroQ[
 Gamma[a + b]/Gamma[a] - Pochhammer[a, b] // FunctionExpand]

Out:= True

I had the wrong impression (maybe from the name) that PossibleZeroQ would be more likely to err by giving false positives than false negatives. I would think that numerical tests would indeed confirm that these expressions are the same. I guess the problem comes from the poles/zero’s of the Gamma function. Or should I really be careful with FunctionExpand and does it really give possibly false results?

P.S. I came across this in a more practical situation where the equality of two expressions was not easy to see by hand but simplified it to this core oddity.

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Recent Questions – Mathematica Stack Exchange

Unexpected behavior of Variables

April 18, 2017 BI News and Info

I apologize in advance if this is a duplicate. Variables has a strange behavior when it encounters powers:

  w = s1^(n + 2) s2;
  Variables[w]
  (*{s1, s1^n, s2}*)

I’d have expected {s1, s2, n},

On the other hand

 w = s1^2 s2;
 Variables[w]
 (*{s1, s2}*)

yields what one expects. I wonder if there is a way to get the expected result in the first example.

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Recent Questions – Mathematica Stack Exchange

Tag Archives: Behavior

B2B Buyer Behavior Is Changing: Making Experience The Top Objective

Understanding key shifts in today’s B2B buying process

Keeping up with the speed and complexity of modern business

Embarking on the digital buying journey

How Suppliers And Supply Chains Change Consumers’ Buying Behavior

Conclusion

Change to Summarized Data Export behavior with Build permission

Effective Email Marketing Uses Data, Behavior, and Customer Personas

Segment Your Audience to Improve Personalization and Deliver Relevant Content

Look at Your Data to See What’s Working (and What’s Not)

Avoid Email Fatigue By Monitoring Audience Behavior

Continuously Revisit Your Strategy for Best Results

Nuance Prediction Service enables brands to forecast customer behavior

Confusing behavior when passing a variable vs. inlining a function call

2 Answers

Pet Family Behavior

Behavior of Possible Zero Q

Unexpected behavior of Variables

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