perfect Archives - Business Intelligence Info

Tag Archives: perfect

What Makes the Perfect CRM Record?

October 30, 2020 Microsoft Dynamics CRM

Want to be a better data-driven marketer? Your Microsoft Dynamics CRM is a great place to lay that foundation!

Let’s get started by talking about what makes your perfect CRM record. This will be unique for every business, but there are some guidelines you can use to start your thinking and planning processes:

Sales and Marketing Alignment—how closely are the sales and marketing teams working together right now?
CRM Architecture—is there a person who can help you understand how your CRM is structured right now, and why it’s structured that way? Learn more about your CRM advocate here.
What are your key marketing goals for this year?
Do you have insight into how your marketing efforts are influencing sales success?
What campaigns are on your agenda for the coming year?

Now Let’s Talk Data!

Once you have answered the above questions, the next step in your data-driven marketing journey is to understand your Customer Journey. Gather your sales, marketing, and customer success teams for a mapping session (you can use our handy map to get started). This can get pretty unwieldy if you try to map all of your journeys at the same time, so we recommend starting with your most successful lead source—is it conferences? Paid search? Referrals? The journey will be unique to each source.

As you map your Customer Journey, you’ll probably notice there are gaps or sticking points where things aren’t working quite as well as they should. Take note of this, marketers! This is where you can apply marketing automation tasks to address those problem areas.

Start Building Your Customer Journey with Our Handy Worksheet >

With your problem areas in mind, you can create a data map that addresses the following items for each campaign type you want to run:

What do you want to achieve with this campaign?
What will data points do you need to run this campaign?
How are you going to get these data points?

Let’s Make the Perfect CRM Record!

Out of the box, your Microsoft Dynamics CRM records will have a standard set of contact fields—but the beauty of Microsoft Dynamics is that you can also create custom fields designed to capture the data points you need for your unique business.

Before you ask for new data fields, talk to your CRM and sales teams to understand what fields are currently capture and which of those are required versus recommended versus optional. In general, a marketing team will need the following fields:

The basics (name, company, title, email address, etc.)
Marketing Lists
Lead Source (required field!)
Source Campaign (required field!)
Record Owner and email address
Contract dates
Competitor Entity data
Industry
Purchase data (Product Entity or custom fields)
Status data (Opportunity, Lead, etc.)

Should You Use the Lead Entity?

That’s really a personal question—B2B organizations might find it easier to keep track of the sales qualified lead to opportunity pipeline using the Lead Entity, but plenty of other organizations struggle with the fact that the Leads Entity doesn’t translate as well to the Opportunity stage—if, for example, you have three or four leads attached to a single Account and Opportunity, it can be difficult to “qualify” all associated Lead records in a single batch.

There are lots of opinions on this, so try to let your sales process guide the conversation and make the technology match what you need to track rather than the other way around.

Other Than Custom Fields, What Do You Need?

Once you create the custom fields you need to power your marketing campaigns, you may want to look into workflows, flows and process to automate your data from Microsoft Dynamics to your marketing automation platform. You might base this off of:

What’s the current path to conversion in Microsoft Dynamics? Does it match your desire path on the Customer Journey?
Where are there opportunities to automate data flow?
What fields are required to move a record forward?
What data points are captured after the Lead record—and can you access them for customer-facing campaigns?

Need a new marketing automation platform to help you on your data-driven marketing journey? Check out the emfluence Marketing Platform for Microsoft Dynamics >

Congrats, Now You’re a Marketing Genius!

Once you have your data structure in such a way that you can pull reports on campaign successes (with sales data!) and easily automate segments of your CRM into your marketing automation platform, you have a great foundation for proving out your worth as a marketer. Now you can:

Improve your strategy with a better budget informed by data from Lead Source and Source Campaign
Improve targeting with data-informed segments
Improve conversion rates with contact scoring (from your marketing automation platform) to reengage cold leads
Engage marketing qualified leads at the right time with sorts on what is and is not in Microsoft Dynamics

You’re well on your way to becoming a better data-driven marketer! If you have more questions or want to see how emfluence can help, be sure to visit us at emarketingplatform.com.

Let’s block ads! (Why?)

CRM Software Blog | Dynamics 365

It’s the Perfect Time to Reimagine Your Business Productivity

October 4, 2020 Microsoft Dynamics CRM

If you’re doing the very best you can to keep your business thriving during a tumultuous 2020, some of this might sound familiar… You’re doing all you can to manage your organization. You have one team member managing the day-to-day financials while working from home full-time. You have another calling on sales contacts while venturing into the office from time to time.

Source

Let’s block ads! (Why?)

PowerObjects- Bringing Focus to Dynamics CRM

Stop waiting for perfect data

August 8, 2020 BI News and Info

In our work with our users, we often find that people across organizations and industries have resistance to getting starting with machine learning until all their data is “perfect”. Arguments about perfect take different forms, but the assertion itself—“I need to wait until X”—is all too common.

In this post, we’ll explain what “perfect data” is and why perfection doesn’t exist, and then walk you through some of the most common objections to getting started so you can make sure you haven’t fallen victim to any of these ways of thinking yourself.

What is “perfect data”?

What makes data perfect? In our experience, when people start talking about waiting until X time in the future or until Y Initiative is in place (codewords for “perfect data”), they often mean that they have every possible or potential parameter accounted for in their dataset. Once they have all of these datapoints, they think, they’ll be ready to undertake a data science project.

The specific data points and parameters that folks want to wait for vary by industry and use case. It might be waiting until there’s a process in place to reliably clean data, or it might be building out a system to understand data access and validate where data is coming from. It could be waiting until a new sensor is installed, or until a new data warehouse schema is finished.

Although there are lots of reasons people think they need to wait to start a machine learning project, if their reasons are about waiting for better data, we know that they won’t stand up to a bit of digging. How can I be so sure?

Because perfection doesn’t exist

“Perfection” is impossible to achieve—your data could always have more data points, it could always be cleaner, you could always have a better understanding of where the data came from, and so on, and so on until you’ve gotten nothing done.

Add to that the fact that, given the ever-evolving nature of data, even if you were to get to a state of perfect data, your data requirements would likely shift by the time a project was finished—and sometimes before a project even gets off the ground. Deciding to wait for perfect is essentially deciding not to reap the benefits of machine learning in your business, meaning that you’re falling behind your competitors.

Common objections

Not convinced? Let’s take a look at a few of the most common reasons we’ve heard for people waiting to start a machine learning project, and why they don’t stand up to scrutiny.

But my data isn’t clean enough!

Data is never clean. Every data point you record has flaws. The reasons are manifold. Customers move to other cities, sensors break, or employees use different spellings for the same word. For a data scientist, it’s normal to work with shortcomings in our data, and something we know how to address.

Although it would be nice if this data-oriented janitorial work wasn’t necessary, in reality, business and data understanding are part of the CRISP-DM process for a good reason. During the analysis, those working with the data will have a chance to get familiar with the data, allowing them to find issues and make a plan to cope with them during model training.

And although the data doesn’t need to be perfect, but obviously needs to fall within certain tolerances. Those tolerances are based on the industry, function, and use case. A pharmaceutical manufacturer is likely to have less error tolerance, while a marketing technology provider might be able to work with a larger degree of error in the data. Data that’s only 75% perfect may be acceptable for tactical, non-critical business predictions.

It’s important to find a middle path to make sure you have useful data without spending an eternity on getting things fixed up front, but it is possible!

But my data isn’t complete!

Data’s never complete. There will always be more information that could help with your use case. The daily annual rainfall at a mining site, the estimated income of your customer households, or the region’s average unemployment rate could all be useful depending on what you’re looking at but might not be readily accessible.

What’s more, adding that information might only have a small effect on your final model—something you won’t know unless you dive in and start the model building process right away. With tools like RapidMiner Go you have the chance to quickly check if your current data set is complete enough to generate value, reduce risk, or cut costs. Don’t wait for the perfect set before you start experimenting!

But my data storage and access pipelines aren’t ready!

Data storage problems will never be fixed. Perhaps a few years ago, IT started creating the perfect representation for your data in your data warehouse. The warehouse has datamarts where users can select and download standardized and validated data easily. If you just wait for another quarter, they’ll have the perfect schema ready for you.

But then there’s a delay. A change in plans. A change in direction. And on and on until you’ve lots thousands of dollars that you could have saved if you’d implemented a machine learning project early on and iterated as things changed.

The point is that centrally maintained data sources are a good idea, but reality is different. You often have more than one source system, and you can’t wait for some central repository to pull it all together.

Plus, you might have some data that’s only available in an Excel file (or handwritten) that you also want to incorporate. Tools like RapidMiner Studio act as a kind of Swiss army knife for data collating data from different sources, without waiting for someone else to make your data look nice.

Then what’s the solution?

Rather than asking “what does perfect data look like”, it’s better to ask “what makes data good enough for my machine learning project to have an impact?” This question is much easier to answer, and to tie directly to business impacts.

If you can have business impact, you should move forward with your project

Even if the impact is small (say, $ 10k/month in cost savings), you’re still having some positive impact, you’re learning how to create and operationalize a machine learning project, and, because machine learning is an inherently iterative process, it will be quicker to get more impact in the future if you already have a model up and running that you can iterate on. There’s no reason to wait, even if your current impact is small.

Don’t overengineer requirements; be agile

Data science, like software engineering, is an iterative process. Don’t expect that your minimum viable product (MVP) will solve all of the problems you have on its first iteration. Sometimes, you have challenging requirements on your deliverables, and you need a final product that is going to both be challenging to build and do a lot when it’s finished.

Nevertheless, it’s useful to build an MVP first, even if it doesn’t check all the boxes. This way, you have a model that you can show to people, and which can demonstrate its impact even if it’s not complete, letting stakeholders see how your project can drive value and reduce risks. If this minimum product is viable it is good, it’s easy to keep iterating and get to your final state—you have more buy-in, and you’re already generating value.

Don’t blindly trust legacy solutions nor wait for the perfect one

“But we have an existing solution that works okay” is something that we often hear. And sure, since you run a business, you must have some solution in place. But AI is revolutionizing the ability of computing systems.

You should always carefully check for current, good-enough implementation against what is possible with machine learning.

The current solution is often something like an Excel sheet with a very simple equation. With a few clicks in RapidMiner, you can easily import all of that and quickly generate something that’s much more impactful than your previous solution.

An real-world example

Take the example of Pontificia Universidad Javeriana, a university in Cali, Colombia. One of the objectives of a long-term strategic plan, the university wanted to strengthen decision making by leveraging the power of machine learning and big data to predict which students were in danger of dropping out of school before finishing their degree.

You can probably imagine some of the countless possibilities for collecting “imperfect data” inherent in such a project. Not only would the data scientists get data from controlled sources like high school and university transcripts, attendance records, and financial aid applications, but they would also be pulling data from outside sources like extracurriculars, social media networks, etc., that could be used to enrich the data set to help make more accurate predictions.

The university IT team, who was responsible for the project, worked with the rest of the administration to develop a set of goals, along with a series of hypotheses that would help explain specific reasons why a student population might drop out. Then, they collected data from sources, both ‘perfect’ from inside the university, as well as ‘imperfect’ from outside, to help support those hypotheses.

Did it work? They think they can easily reach a 10% reduction in the student dropout rate.

The effort of finding and using perfect data has to be measured against the value of waiting to use it. In the case above, the university realized that the value of rapid recognition and intervention into dropout-risk students far outweighed any causes for delay. And, if more data becomes available (for example, as the pandemic changes enrollment patterns), they’re ready to iterate quickly to improve their model.

Wrapping up

An iterative approach is the most logical one when dealing machine learning, and that’s especially true when dealing with imperfect (read: all) data. Business leaders should consider what process they want to improve, or which business decisions would benefit from predictive analysis approach, and dive right in to see what kind of impact they can have.

Then, together with the data science and/or IT teams, they can look at what data is needed, the sources of that data, and how that data can be translated into practicable information and predictive analysis.

Would you like help figuring out how to make use of your imperfect data to improve your business? Sign up for a free, no obligation AI assessment and we’ll help you build a plan for using machine learning to tackle some of your most challenging business problems.

Let’s block ads! (Why?)

RapidMiner

The most perfect pupper tattoo

May 29, 2020 Humor

Posted by Krisgo

via

About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon

Let’s block ads! (Why?)

Deep Fried Bits

A PERFECT EXAMPLE OF THE LIBERAL THOUGHT PROCESS

May 13, 2020 Humor

Let’s block ads! (Why?)

ANTZ-IN-PANTZ ……

Rudy was joking (says his lawyer) when he said he had insurance, since his call to Trump was perfect

November 28, 2019 Humor

The joke is in your hands.

The attorney, Robert Costello, said Giuliani “at my insistence” had called Trump “within the last day” to emphasize that he had not been serious when he said he had an “insurance policy, if thrown under the bus.”

“He shouldn’t joke, he is not a funny guy. I told him, ‘Ten thousand comedians are out of work, and you make a joke. It doesn’t work that way,’” Costello told Reuters.Giuliani has already said that he was being sarcastic when he made the comments. Trump, too, has brushed them off, telling reporters in the Oval Office this week that “Rudy is a great guy.” The White House declined to comment on Costello’s remarks.

www.reuters.com/…

x

I have to admit, when I heard Giuliani say he had Ã¢ÂÂinsuranceÃ¢ÂÂ (and by his demeanor you could tell he was dead serious) my mind went right to the fact that Rudy was US Attorney in NY and Mayor of NY for a combined 13 years. How much Trump dirt/business illegality must he know? https://t.co/usPar0MUYe

— Glenn Kirschner (@glennkirschner2) November 27, 2019

Subpoenas issued by federal prosecutors in recent weeks suggest a sweeping investigation is being conducted into Rudy Giuliani and his associates, with potential charges including obstruction of justice, fraud and money laundering, the Wall Street Journal first reported and the Washington Post confirmed.

What we know: Prosecutors have issued subpoenas seeking records and information related to Giuliani and two associates, Lev Parnas and Igor Fruman, who have already been indicted on campaign finance violations. The investigation is being led by the FBI and the Southern District of New York, the U.S. attorney’s office that Giuliani once ran.

Giuliani has denied any wrongdoing. Parnas and Fruman have pleaded not guilty.

Prosecutors are also seeking information about the pro-Trump groups America First Action and America First Policies.

The subpoenas suggest that the following charges are being considered, according to the Journal.

Obstruction of justice

Money laundering

Conspiracy to defraud the United States

Making false statements to the federal government

Serving as an unregistered foreign agent

Donating funds from foreign nationals

Making contributions in a false name

Mail fraud

Wire fraud

www.axios.com/…

Let’s block ads! (Why?)

moranbetterDemocrats

Perfect pancake procedure

January 13, 2019 Humor

Posted by Krisgo

via

About Krisgo

Let’s block ads! (Why?)

Deep Fried Bits

Indeed’s ultimate hiring solution is to get you ‘a perfect one-to-one match’

October 24, 2018 Big Data

The resume is a relic. And the time spent in typical interviewing of candidates is highly inefficient. So how does one improve the equation for both the employer and the potential employee to make it more efficient and fair?

At VB Summit 2018, Darren Nix, group manager at Indeed, says AI and machine learning are already helping increase recruiter efficiency by as much as four times. As a result, its approach has helped both candidates and recruiters focus more time on making the human connections count so the hires are a much better fit.

Ultimately, Indeed’s vision is to strip down the whole hiring process to its core foundation and then rebuild it from the ground up using AI and automation. “We view the ultimate solution as trying to create a perfect one-to-one match,” said Nix. “Right now we’re at more like a 50:1 match, or a 20:1 match where candidates are having to apply to lots of jobs in order to find that perfect match.”

“The way that we measure our success is how much faster and more efficient can we make that process for the job seeker,” Nix said. The hope is to get the recruitment done by which the applicant only has to apply to five jobs to find the perfect job fit.

Nix says it’s all about being able to add a lot more robust and provable data about the employer as well as the job seeker. By doing so, the employer can actually feed a much better data set into the algorithm to get a smart answer.

Looking ahead, emerging technologies are a big part of the radical transformation of how folks are hired. But the full realization of those changes could be potentially scary to hiring candidates. Instead of providing a big bang solution to the market, which would potentially only attract a small number of early adopters, Nix said Indeed aims to improve the process of recruitment and hiring with each new release.

Let’s block ads! (Why?)

Big Data – VentureBeat

The perfect date

April 26, 2018 Humor

Posted by Krisgo

The perfect date

Via Twitter

About Krisgo

Let’s block ads! (Why?)

Deep Fried Bits

Office 365 and Dynamics CRM: The Perfect Match

March 15, 2018 CRM News and Info

As innovative technologies emerge, the world of business continues to shift and evolve. Keeping up with these changes isn’t easy. To succeed, your organization needs the right tools.

The Microsoft Office 365 suite is far and away one of the best tools for maximizing your company’s productivity. There are a lot of things to love about Office 365: you can access it from any location, collaboration between team members is easy, and your data is always secure. And, most of all, Office 365 connects seamlessly with your existing Dynamics 365 CRM.

JourneyTEAM, based out of Utah and Tennessee, has deep experience in helping organizations integrate these tools into how they currently do business. Let’s look at why Office 365 is the right choice for your organization. Then, we’ll explore how it can integrate with Dynamics 365 and how JourneyTEAM can help you leverage the maximum efficiencies out of these tools.

Access Office 365 from Any Location
One of the best things about Office 365 is its portability and accessibility. You don’t need to worry about syncing between different devices and locations, Office 365 does it for you. Whether you’re working on a document in Word, an Excel sheet, or an Outlook email, you can rest assured that you’ll have access to it anywhere and everywhere. Need to throw together a presentation? Simply grab a PowerPoint file from the cloud, and you’re in business.

Collaborating is Easy
Whether your organization is large or small, your team’s ability to collaborate efficiently and effectively can mean the difference between success or failure with any given project. Office 365 understands this, and Microsoft has made it easy for your team to work side-by-side, even if its members are separated by thousands of miles of physical distance. Thanks to Skype Meeting Broadcast, Delve, SharePoint, and other Office 365 apps, you can collaborate with ease.

Top Notch Security
No matter what field you’re in, running a business can be a challenge. That’s why it’s so important to choose a software solution that minimizes the number of things you have to worry about, like the security of your data. Office 365 offers built-in security features, including privacy controls and compliance tools. With Office 365, you’ll never have to worry about the safety of your data.

Integration with Microsoft Dynamics 365
Of all the reasons that Office 365 is such a great choice for your business, this has to be at the top of the list. Office 365 integrates quickly and easily with Microsoft Dynamics 365 CRM.

What does this look like? Let’s take an example. You get an email delivered to your inbox, and you need to track the customer data it contains. Whether the email comes from a lead or a current client, all you have to do is select the CRM tracking option in Office 365. As a result, customer data is automatically synced with Dynamics 365 CRM. It doesn’t matter what device you’re using, either. Whether you’re on your laptop, your office computer, or your phone, all it takes is the click of a button to integrate new customer data with your Dynamics CRM.

Thanks to its seamless integration with Dynamics CRM, Office 365 can dramatically improve your organization’s efficiency and workflow. Are you ready to learn more about how Office 365 can work for you? Contact JourneyTEAM now to set up a free consultation!

—————-

Article by: Dave Bollard – National Director of Marketing

JourneyTEAM is an award-winning consulting firm with proven technology and measurable results. They take Microsoft products; Dynamics 365, SharePoint intranet, Office 365, Azure, CRM, GP, NAV, SL, AX, and modify them to work for you. The team has expert level, Microsoft Gold certified consultants that dive deep into the dynamics of your organization and solve complex issues. They have solutions for sales, marketing, productivity, collaboration, analytics, accounting, security and more. www.journeyteam.com

Let’s block ads! (Why?)

CRM Software Blog | Dynamics 365

Tag Archives: perfect

What Makes the Perfect CRM Record?

Now Let’s Talk Data!

Let’s Make the Perfect CRM Record!

Should You Use the Lead Entity?

Other Than Custom Fields, What Do You Need?

Congrats, Now You’re a Marketing Genius!

It’s the Perfect Time to Reimagine Your Business Productivity

Stop waiting for perfect data

What is “perfect data”?