loyalty and customer marketers use predictive analytics

3 Ways Loyalty & Customer Marketers Use Predictive Analytics to Stay Ahead

With everything going on in the world today, businesses find themselves in new and uncharted territory.  Given what industry your business is in, some loyalty and customer marketers have had a very challenging year trying to forecast outcomes and keep retention numbers high despite so many external factors impacting projections. As a result loyalty and customer marketers have taken an interest in predictive analytics to help them address some of their challenges by using their data to craft forward-looking offers, programs, and forecasts.

Relying solely on historical data to predict retention, any upsell or cross sell opportunities, or even your churn number may feel impossible right now.  Luckily, emerging software companies are making these heavy workloads feel a bit lighter.  No code predictive analytic software was invented by Squark and has become a popular tool the second half of this year for those exact reasons.  Your historic data doesn’t currently help you plan because this past year may look like a bunch of scribbles on paper that was crafted by a three year old, lots of ups and downs.

So how do you stay ahead? How do you know which customers will respond to your offers, how are you predicting that outcome?  How do you currently score for loyalty, is that model even accurate right now?  Lastly, the number that we all check daily – churn.  Do you have any data-backed churn indicators?  Or do you know when your team should be reaching to at risk customers before they send that “I’m sorry but…” email to their customer success rep?

Below are only three of the many ways loyalty and customer marketers are applying no code predictive analytics to their day-to-day and planning routines to stay ahead of this rapidly changing business environment.

  1. Identifying which customers will respond
    The simplest way to understand a future customer outcome is to classify them based on likelihoods to do something, like download a white paper or attend a virtual event.  Predictive software such as Squark, can predict any event for which you have data on. This allows you to plan a bit smarter based upon the predictive data outcomes.  You’ll be able to hopefully increase conversions and have more confidence in obtaining your program goals.
  2. Scoring Customer Loyalty
    You may already generate or create a unique customer loyalty score.  Whether using rules in a system like your marketing automation or CRM, or even your own mechanisms and logic.  In that case, Squark can take your scoring model to the next level with predicting drivers and forecasting future loyalty.  Using complex scoring systems from customer lead score, NPS, or a combination of a few systems you can confidently predict who is likely to be retained and what offers will keep them converting.
  3. Predicting Churn
    With historic data about which customers have lapsed or stopped entirely being customers, it is possible to score exactly who will churn and why.  Armed with this knowledge loyalty and customer marketers can ensure at-risk customers are addressed sooner and quickly to mitigate revenue loss.  Knowing who may be at risk you can craft a tailored program just for those accounts to make sure you stay top of mind and open dialogue.  On the other hand, it’s nice to have that information so that you can actively pull them out of other campaigns that may not be suitable for their account at that time.

There are many other use cases where no code predictive analytics can support loyalty and customer marketers.  However, these three examples are being used more and more so that it can take the guessing out of planning and make the most out of every marketer’s time.

With shifting strategies and new priorities, it can be a lot to support and very time consuming. Take advantage of no code analytics to reclaim lost time due to data prep and cleaning, analytics, and projection planning.  Be confident in your programs’ success.  And lastly, software such as this allows you to create programs ahead of the curve so that you’re offering items or services that your customer is likely interested in, creating a happier and healthier client base (and hopefully NPS).

Predictive Analytics Powers Customer Loyalty

3 Ways No Code Predictive Analytics Powers Customer Loyalty

Customer loyalty is where long-term value is created. No code predictive analytics gives marketers a easy to use way to predict what their customers will do next, without coding.

CMOClub-Most-Critical-Skills-Gaps-Feb2020

Are CMOs Wrong About Data Science Skills Gaps?

Chart Source: The CMO Club, Feb. 2020

In this February 2020 survey, The CMO Club asked it members, “What are the most critical skill gaps in your organization? Select up to 3.” 78% chose Data Science, the top answer. The reason is clear. Data science knowledge is seen as indispensable to predictive marketing—the key to improving customer journeys, increasing satisfaction, and building sustainable, profitable revenue streams. For example, optimized cross selling, content personalization, message timing, and ad effectiveness measurement are perceived at the top as the province of data scientists.

For some sophisticated predictive problems this may be true, but for the majority of ordinary marketing predictions it is old news. The advent of practical AI and machine learning has made predictive tasks feasible for most organizations without data scientists.

So why has adoption of AI and machine learning for marketing been slow, especially in the mid- to small-size companies? Several reasons are often cited*:

  1. 57% of analysts say allocating budget was a challenge
  2. 48% of analysts say getting upper management on board was a challenge
  3. 43% of analysts say training and recruiting staff with the right skills was a challenge
  4. 39% of analysts say knowing where to start was a challenge

*Source “AI for Retail Marketers USPS Research,” USPS & SIS International, June 2019.

Why do these roadblocks still exist when there are a number of cost efficient, versatile and ease to use solutions available to immediately breakdown these impediments? Let’s address each separately:

57% of respondents said allocating budget was a challenge

Budget allocation is a perennial challenge, and always a poor excuse for inaction. After all, if a new technique delivers better results with demonstrable ROI, who would object? Reasonable business people don’t.  The real budget objections are perceptions that:

  • High cost consultancies or big brand/high cost software will be required, putting return on investment out of the question.
  • Data science teams will need to be built, with scarce talent and high salaries.
  • Expensive projects have failed to deliver to date.

All three of those points had been obstacles, but aren’t any longer. Low-cost tools are available that not only offer the flexibility to answer a large range of marketing questions, but also can be implemented—without data scientists or programmers—in hours vs. weeks. The versatility of these tools allow organizations to equip multiple analysts to make action-oriented predictions easily.

It is time to revisit budgets. Efficiency can be gained nearly instantly within the resources of even small companies.

48% of respondents said getting upper management on board was a challenge

This one is easy to understand. Management teams have been promised results that failed to materialized after large projects and massive expenditures. Why will it be different this time?

Fortunately, this obstacle is simple to overcome. Automated machine learning makes attacking a proof-of-concept straightforward and fast. Nothing speaks truth like results. Read How to Prove AI Value to Skeptics in our AI Tips archive for more.

43% of respondents said training and recruiting staff with the right skills was a challenge

This one is also based on old information. Solutions built to provide predictive marketing power to analysts are specifically designed with them in mind. That means there is no recruiting. Existing teams can now act on forward-looking data without writing a line of code. It is as simple as using a spreadsheet. Training expenses are minimal for applications that are self-service for anyone with basic analytical skills.

39% of respondents said knowing where to start was a challenge

Starting points can be difficult to find when processes are complicated. The first step is always “learn the process.” For approaches that require data science knowledge and programming skill, there is no simple path for analysts who do not have legions of specialists available. Once the process becomes as straightforward as uploading a few data sets and inspecting results minutes later, experimentation becomes an automatic starting point.

Knowledge gained in the first few runs reveals whether data is sufficiently broad and deep, if the predictive question is appropriate, and how the results can be translated to actions. Incremental tries with tweaked data and assumptions are fast, which helps analysts converge on a solution quickly. Low overhead means refinements are low effort and cost.

The take-away is that CMO’s do not have to fear lack of data science and analytical talent. AI and machine learning tools are available now to begin answering critically valuable questions with existing teams. Misconceptions may seem stubbornly persistent, but C-level execs did not reach their positions because they are resistant to change. They just need good information on alternatives. Give it to them.

Squark is available free to produce your own evidence.

AI Predictive Marketing in Reach for Analysts

Predictive marketing is the practice of anticipating your prospects’ next moves in order to improve customer journeys, increase satisfaction, and to build sustainable, profitable revenue streams. For example, optimized cross selling, content personalization, message timing, and ad effectiveness measurement demand accurate predictive analyses. The advent of practical AI and machine learning has made these tasks feasible for most organizations.

So why has adoption of AI and machine learning for marketing been slow, especially in the mid- to small-size companies? Several reasons are often cited*:

  1. 57% of analysts say allocating budget was a challenge
  2. 48% of analysts say getting upper management on board was a challenge
  3. 43% of analysts say training and recruiting staff with the right skills was a challenge
  4. 39% of analysts say knowing where to start was a challenge

*Source “AI for Retail Marketers USPS Research,” USPS & SIS International, June 2019.

Why do these roadblocks still exist when there are a number of cost efficient, versatile and ease to use solutions available to immediately breakdown these impediments? Let’s address each separately:

57% of respondents said allocating budget was a challenge

Budget allocation is a perennial challenge, and always a poor excuse for inaction. After all, if a new technique delivers better results with demonstrable ROI, who would object? Reasonable business people don’t.  The real budget objections are perceptions that:

  • High cost consultancies or big brand/high cost software will be required, putting return on investment out of the question.
  • Data science teams will need to be built, with scarce talent and high salaries.
  • Expensive projects have failed to deliver to date.

All three of those points had been obstacles, but aren’t any longer. Low-cost tools are available that not only offer the flexibility to answer a large range of marketing questions, but also can be implemented—without data scientists or programmers—in hours vs. weeks. The versatility of these tools allow organizations to equip multiple analysts to make action-oriented predictions easily.

It is time to revisit budgets. Efficiency can be gained nearly instantly within the resources of even small companies.

48% of respondents said getting upper management on board was a challenge

This one is easy to understand. Management teams have been promised results that failed to materialized after large projects and massive expenditures. Why will it be different this time?

Fortunately, this obstacle is simple to overcome. Automated machine learning makes attacking a proof-of-concept straightforward and fast. Nothing speaks truth like results. Read How to Prove AI Value to Skeptics in our AI Tips archive for more.

43% of respondents said training and recruiting staff with the right skills was a challenge

This one is also based on old information. Solutions built to provide predictive marketing power to analysts are specifically designed with them in mind. That means there is no recruiting. Existing teams can now act on forward-looking data without writing a line of code. It is as simple as using a spreadsheet. Training expenses are minimal for applications that are self-service for anyone with basic analytical skills.

39% of respondents said knowing where to start was a challenge

Starting points can be difficult to find when processes are complicated. The first step is always “learn the process.” For approaches that require data science knowledge and programming skill, there is no simple path for analysts who do not have legions of specialists available. Once the process becomes as straightforward as uploading a few data sets and inspecting results minutes later, experimentation becomes an automatic starting point.

Knowledge gained in the first few runs reveals whether data is sufficiently broad and deep, if the predictive question is appropriate, and how the results can be translated to actions. Incremental tries with tweaked data and assumptions are fast, which helps analysts converge on a solution quickly. Low overhead means refinements are low effort and cost.

The take-away is that misconceptions are the reasons many marketing analysts don’t use AI and machine learning for predicting critically valuable questions. There really is nothing stopping analysts from generating predictive actions with today’s SaaS solutions.

Learn AutoML for Free

Right now, we all need new ways to approach work, education, and creativity. Squark is contributing by offering everyone free access.

This is a perfect time to explore the ways AI and Machine Learning can improve your world.

Learn Something New

For all the buzz about Automated Machine Learning, only single digits percentages of those who could benefit from it actually have tried. Routine obstacles like existing deadlines, established procedures, and approval for expenditures are the culprits. Now they are gone. We are all forging new “normals.” Access to Squark AutoML for free removes the final barrier.

Solve a Problem

Many solutions that were previously impossible are now simple with the power of AI. You can prove this for yourself instead of speculating and researching. The big breakthrough is that Squark is a no-coding way to apply AI to find answers.

Change a Mind

Maybe you and your colleagues are not convinced that AI and Machine Learning deserve the hype they get. Find out for sure by producing results – or not. We’ve removed the overhead.

Make a Discovery

Valuable, new applications for predictive AI can be invented by lots of smart people like you. With free access to codeless AI, you’re freed to find beneficial uses for in automated machine learning in research, business, education, entertainment, or any discipline deserving of your talents.

Let’s go. Register for a login now.

Codeless AI Transformative for Direct Mail Agencies

Codeless AI leverages agencies’ strengths to drive hyper-personalization of direct mail. The results are significant uplifts in campaign effectiveness  with large reductions in costs.

The Business Challenge: Achieve marketing program objectives while reducing costs.

In case you think those glory days of direct mail are over, consider this: Direct mail is a nearly $50 billon industry in the US, with advertisers achieving 5% response rates and 1,300% ROI. Volume is at all-time highs.

Success in direct mail depends on knowing your target and deploying the right message and creative for the right individual. Great agencies and marketers are the ones that combine analytics and creative to incite action. Even when analysts, strategists, and creative teams are in the “best” category, controlling costs to improve return on program investment is the number one challenge agencies face. Firms that employ artificial intelligence to get an edge in that quest are now the highest performers.

Hyper-personalization—sending the strongest messages, in the best formats, at the correct times, to the right people—pays big dividends. It makes sense. If you know what types of marketing materials will resonate with a target, you can tailor your package to match. If you know the likelihood of response by recipient, you can optimize spend. Most of all, if you know which messages resonate from brain to wallet for each recipient, you gain repeat revenue from target audiences.

The Transformation: AI power without data science teams

Squark helps agencies hyper-personalize client campaigns. Understanding target audience behaviors are essential to creating effective campaigns, but mere insights are not sufficient. Practical implementation of hyper-personalization means predicting exactly what message and package will prompt an individual to respond. The essence of hyper-personalization is making each person a segment of one. That’s what increases response rates, and what propels cost reductions in customized printing, mail shop services, and bulk postage. Knowing the who, what, and more important than ever.

The Squark process is simple. Data sets containing known outcomes from past mailings are uploaded directly to Squark with a few clicks. That’s the training data. A production file is then uploaded. That’s the set of records on which to predict. Squark then builds and compares scores of models. The most accurate algorithm is then used to produce row-by-row predictions on the production data and they are returned to the agency.

Machine learning detects patterns in existing data and predicts how never-before-seen recipients will behave. In addition, Squark ranks factors in order of their predictive importance, so that agencies automatically discover which features are critical to a campaign’s success. Personas revealed from predictions make it easier for creative teams to produce appropriate messages and imagery that are then incorporated into the fundraising strategy.

Squark is direct, clear, fast, and intuitive to use. Squark cuts modeling time down from the weeks it could take with custom programming to just hours. Dozens of iterations and refinements can happen in a fraction of the customary time. This is the essence of how Squark moves past insights to drive actions. Actionable output feeds actual campaigns.

The Results: More net revenue; Better engagement.

Squark drives immediate uplift in direct marketing campaign response through better targeting—engaging the right prospects with the right messages. Production and mailing costs drop significantly by avoiding people who discard pieces without consideration. That is the simple formula for monumental increases in return for large campaigns.

AI or Not?

How to Tell What’s Real

Have you tried the new “AI Latteccino” at Starbucks? Absurd—and not real, of course. But don’t be surprised if food and beverage companies become the latest vendors to co-opt the Artificial Intelligence mantle. Nearly every technology company claims that AI is “at the core” of their offerings. Actually, few use anything resembling true AI. By repackaging classical statistics and data analytics, many companies add useful functionality but fail to produce the revolutionary value that real AI can deliver.

In his July, 2018 article in Fortune, Arif Janmohamed of Lightspeed Venture Partners cautions, “A number of SaaS and automation companies out there are positioning themselves under the AI banner, even though all they really do is use data analytics to orchestrate applications and workflows. The technology doesn’t get more intelligent over time, and it never reaches the level of autonomy of bona fide AI.”

AI and Machine Learning

Artificial Intelligence defines computer systems patterned after human intelligence in their ability to learn and recognize—so that previously unseen information can be acted on in ways that produce useful results. This is distinctly different from systems that merely organize, report, and help visualize what has already happened. As interesting as insights may be, reporting-based analytics cannot make the leap to project those observations onto new, never-before-seen data.

Machine Learning is the subset of AI that solves complicated analytical problems without needing to be explicitly programmed for each task. By learning patterns in data through sophisticated AI algorithms, Machine Learning can build accurate models and apply them to brand new data. This is the breakthrough that allows formerly impossible analyses to be made practically and quickly.

“Explicit programming” is slightly misleading in the definition of machine learning, because it does not necessarily mean “no programming.” It refers only to the general usefulness of analytical algorithms without regard to the data being targeted. Until recently, managing setup, loading, processing, and output of analytical results with machine learning has required plenty of programming—even learning new languages expressly created for those purposes. Fortunately, Squark has removed this final obstacle to rapid, productive use of Machine Learning. Squark is completely codeless Automated Machine Learning (AutoML).

Why Is AI Better?

Artificial Intelligence in the form of AutoML solves analytical problems faster and with more concrete results. AI has the ability to build models from known outcomes and to project these patterns onto new, future data. It really does work. Predictive value of AI is proven to be faster and far more useful than conventional, statistical analysis.

AutoML gives businesses their first, real tool for augmenting intuition with data science. Finally, we there are ways to apply imagination with confidence. AutoML frees organizations to do what they do best; what they really want to do in place of system wrangling. Marketing, for example, is the hottest application for AutoML right now because results are easy to monetize. Marketers become free to market. Armed with predictive knowledge, actions are easier to implement.

  • Which visitors are likely to abandon carts?
  • Which ads work best in which media?
  • What leads will produce the highest customer lifetime value?
  • Are there cross-sell/up-sell offers that will boost revenue?
  • What digital marketing actually accelerates customer journeys?

It isn’t magic. AutoML is merely automation of classical AI computational methods within an online application made for business users. Just like hand-made AI, AutoML can find the patterns and associations that have always been present in clients’ data.

How AutoML Works

Squark uses an AI process called Supervised Learning, where algorithms learn from sets of known outcomes and build models that can be applied to new, never before seen data. Like a bloodhound getting the scent, it can pick out data that foretells similar outcomes—clues that cannot be deciphered with conventional programming logic.

How it accomplishes this is the stuff of arcane algorithm builders. That is does it is provable. By trying, testing, refining, and testing again—perhaps thousands of times—Squark builds remarkably accurate models that can be measured against hold-out data. If a model can predict accurately on data that it has never seen, but which we know to be true, then it is proven to be generally useful for future predictions. That is exactly what happens in the Squark AutoML process.

Squark takes a cold, unbiased look at all the data and builds accurate models no matter how many elements there are. There are no formulas to build and maintain. The data tells its own story. Acting on the latest data, AutoML automatically takes the newest information into account and disregards factors that no longer predict success. It isn’t magic or mystery.

To train itself, Squark ingests sets of data that represent known outcomes, such as lead tables for the previous period. Lead tables typically show the basics—whether or not a lead converted to a different stage, became an opportunity, or closed won or lost. They may also contain many more values related variously to industry, company size, competitive status, time and date, inbound and outbound activity, or hundreds of other traits. Clicking a column in the table tells the AutoML which variable you want to predict. Will the lead convert from marketing qualified to sales qualified? Is likely customer lifetime value forecast higher or lower than target? Which one of five possible messages will elicit a response?

The next step is to link the new data, the leads on which you want to make predictions. Squark learns the patterns and builds scores of models, testing them against one another to find the best. Running the new leads through the winning the model then happens quickly—minutes, typically. Output is a table with predictions appended, including probabilities for each record. Now you have a prioritized list of prospects that is richer than any scoring formula could provide.

Afterword

If you have delved into the world of AI data science already, you know that they really is complicated. Here is a brief summary of some of the complexity that is abstracted by Squark. Those who understand technical details can see that advanced AI data science is being employed to prepare data and tune performance. Those unfamiliar with the terminology will be pleased to know they do not have to learn it.

Squark does much of what an experienced data scientist would do to make sure data is structured for predictions and that the system is adjusted for the peculiarities of unique data sets, including:

  • Data Preparation – Cleaning and normalizing data columns (features).
  • Feature Extraction – e.g. Turning a single date/time feature into year, month, day, day of week, and time to make infirmed predictions.
  • Feature Selection – Identifying features that are most predictive of outcomes.
  • Algorithm Selection – Applying particular AI algorithms that are suited to making a prediction on particular data.
  • Hyperparameter Optimization – Tuning the algorithm execution process for efficiency and preventing runaway calculation.
  • Cross-validation – testing algorithms’ predictions against a hold-out set of know outcomes to validate accuracy.
  • Leakage Detection – ensuring that data sets do not contain the values to be predicted, which would invalidate results.

It is less important to understand the details of how these data prep capabilities work than to be certain they are working on your behalf. If your data is imbalanced—meaning you don’t have a lot of outcomes from which to learn—AutoML can compensate for that. If you think your data is dirty or too sparse, or has missing values, AutoML has ways to detail and prepare your data for use. Remember that the goal is to produce reasonable predictions that are more accurate than guessing and timely enough to be useful.

 

Closing More at Lower Cost With Accessible AI Predictive Power

Business leaders aren’t looking for more raw information. They’re drowning in data.  Between 60% and 73% of all data within an enterprise goes unused for analytics according to Forrester.  Answers are what is in short supply. Those responsible for revenue growth have to balance the value of insights within that data, from the cost of extraction.  Here are the options …

  • Reports, Charts & Graphs
  • Purpose-built Tools 
  • Custom Data Science (AI & Machine Learning)  
  • AutoML (Automated Machine Learning)

Reports, Charts & Graphs are the meat & potatoes for most sales organizations.  While the attribution for the famous quote about managing only what you can measure is up for debate, the maxim is not. Identify a few key inputs, graph it over time to show you where you’ve been and how some stats relate to one another. This may be more or less automated by BI systems and dashboards, but the result is a clear look in the rear-view mirror. You bump into unexpected things driving that way.

Purpose-built Tools were quick to identify the biggest problem with AI & machine learning. Even though most users knew of AI’s power, few knew how to apply it to grow top-line revenue. These solutions were quick to grab an algorithm and apply it to a specific use-case like lead scoring, sales coaching, or identifying churn risk in your existing customer base. While these point products are often worth the investment, sales organizations are wary of yet another subscription to solve a problem that is rooted in data analysis. With AI becoming essential across the spectrum, the idea of buying multiple, AI-embedded layers in the sales and marketing tech stack is a frightening for several reasons:

  • Up to half of AI startups are actually not using any AI at all according to a study that evaluated 2,830 startups.
  • AI embedded in apps is often opaque. “Take our word for it, these are your hottest opportunities” is a big leap if you can’t see why they were ranked that way.
  • Layers of AI may produce conflicting answers, with no way to vet or normalize them.

Custom Artificial Intelligence & Machine Learning have dominated the conversation lately. Unfortunately, many tools built by and for data scientists and programmers are impossible for business people—even data-savvy analysts—to use. Coders gonna’ code. While large businesses can afford to throw massive resources at this, CXOs and sales leaders should bear in mind the prohibitive expense of staffing the “sexiest job in the 21st century”. Peek under the veneer of Google, Amazon, and Microsoft Ai and machine learning offerings and you’ll quickly discover eye-glazing references to GitHub and Jupyter notebooks and Python code.

AutoML Fortunately, there is an alternative for applying machine learning without using data scientists or their complicated tools of the trade. A new type of technology (AutoML) allows organizations to forgo much of the process by automating data preparation, feature engineering, and model creation. CROs, analysts, operations and sales enablement leaders can arrive at insights faster and provide their teams huge advantages.

Squark offer AutoML that can be applied across the entire sales operations and marketing decision making processes. Schedule a free assessment call with Squark to learn how we can help, or download our briefing, 14 Ways to Drive Sales Performance Using AutoML.

Why AutoML Beats Lead Scoring Formulas for B2B

CRM and Marketing Automation systems have offered lead scoring features for decades. The notion of using arithmetic to turn qualification criteria and behavioral data into a simple-to-consume number makes sense. Business development and sales teams always appreciate guidance on which leads to follow next to maximize productivity. In practice, few organizations do excellent lead scoring, and it isn’t their fault.

Traditional lead scoring first assumes that you understand all the moving parts that indicate buying intent. BANT-style rating of readiness is important. Responses to emails, website visits, and social media tell a story. External databases of purchase sentiment are valuable. How much does each contribute? That is really difficult to determine in advance, so you take guesses and refine the model based on experience.

Scoring models also require constant tweaking. Elements calculated by legacy scoring models are increasingly complicated to monitor during the customer journey. Information that you already track changes rapidly, and new data appears that wasn’t counted before. Since scoring model updates are largely a manual process, iterations are time-consuming and error-prone. Even if you understand the interplay of variables perfectly, writing Boolean logic to express them accurately is nearly impossible. Tracing scoring model improvements back to KPIs is also tricky, which makes prioritizing maintenance effort an even greater challenge.

Automated Machine Learning (AutoML) is much better at ranking leads because it takes a cold, unbiased look at all the data and builds accurate models no matter how many elements there are. There are no formulas to build and maintain. The data tells its own story. Acting on the latest data, AutoML automatically takes the newest information into account and disregards factors that no longer predict success. It isn’t magic or mystery. Here’s how it works…

To train itself, AutoML ingests sets of data that represent known outcomes, such as lead tables for the previous period.  Lead tables typically show the basics—whether or not a lead converted to a different stage, became an opportunity, or closed won or lost. They may also contain many more values related variously to industry, company size, competitive status, time and date, inbound and outbound activity, or hundreds of other traits. Clicking a column in the table tells the AutoML which variable you want to predict. Will the lead convert from MQL to SQL? Is likely CLV forecast higher or lower than target? Which one of five possible messages will elicit a response? Think of any yes-no, in-out, or how much question.

The next step is to upload the new data, the leads on which you want to make predictions. The AutoML system learns the patterns and builds scores of models, testing them against one another to find the best. Running the new leads through the winning the model then happens quickly—minutes, typically. Output is a table with predictions appended, including probabilities for each record. Now you have a prioritized list of prospects that is richer than any scoring formula could provide.

In addition, AutoML shows which variables were most important to its predictions. This ranked list of variable importance amounts to a description of your ideal prospect persona. You will confirm what you knew, and maybe discover parameters that surprise you. This level of actionable insight is invaluable in targeting outreach to accelerate your pipeline. The world is too complex to rely solely on algebra. Taking advantage of the remarkable ability of AutoML to see relationships across vast data stores is the way to go. Give AutoML a try to see how forgetting to update scoring formulas could be your new, top priority.

Creating Marketing Value With Codeless AI

Earlier this year, Brett House of Nielsen and Judah Phillips from Squark presented at Analytics Nexus 2019. Here is a transcript of Brett’s remarks.

Watch the video

“Thank you, Judah. To quickly introduce myself, as Judah had mentioned, I run product marketing and SaaS demand generation for the Nielsen DMP and the Nielsen Marketing Cloud. I’ve done this for the last few years, and what we’re looking at here in this particular slide to give you a specific use case on how we’re using Squark, and predictive AI to better our ability to effectively generate revenue for our sales organization.

And this slide sort of sets the context for that. We’re looking at a B2B marketing demand funnel. From inquiry, which is sort of initial conversion, all the way through closed deals. We really needed to understand, as a marketing organization, how to better predict what drives prospects and new audiences through the demand funnel. What brings and initial inquiry to the Nielsen Marketing Cloud or the Nielsen DMP to actually being accepted as a sales lead and closed as a deal, which we can then attribute back to our own activities?

It really comes down to three, core criteria, which, very simply put, are understanding how we can predict WHO is most likely to become a sales qualified lead and a revenue point for the Nielsen Marketing Cloud, which comes down to personas. Who are we targeting? What is their title? What is the industry vertical that they are a part of? What is their function within their company? And which one of those attributes is most predictive of someone’s purchasing one of our products.

Number two is really WHAT. What kind of content are they engaging with from a B2B marketing perspective? We develop content that runs the gamut, from webinars, to owned events, to reports and white papers, to bylines and videos, to podcasts, and we’re tracking every touchpoint…

…across what you see as number three, WHERE these particular personas are interacting and engaging with our brand and with our product. And that’s really the channels of engagement. Media channels like pay-per-click advertising or display advertising; owned channels like nielsen.com product pages and solutions pages for our various products; social media, which could be earned content. And we’re tracking each one of these engagements through each one of these three criteria to understand who is most likely going to purchase a product from us and who is exhibiting purchase intent.

What Squark has really allowed us to do is to be more predictive of that flow from the top of the funnel, when someone downloads a white paper, or RSVPs to a webinar, or commits to an initial action, all the way through to the qualification phases which you see in the middle of the funnel, which are marketing qualified and sales qualified.

Marketing qualified we define as someone who fits two key metrics. One is an engagement metric, meaning how often have they engaged with us and where are they engaging with us? So, it’s really number two and number three – the what and the where. Whereas the fit metric, which is another way that we help predict purchase intent, is really number one – the who. Who is this person? What is their title? What is their function? And how does that help influence the purchase decision, and that path to purchase – all the way down to the bottom of the funnel?

At the end of the day, the whole purpose of this is to increase the size of the sales pipeline from a B2B marketing perspective, and it’s very reminiscent of what you see in B2C marketing as well. We look at every stage in that demand funnel. Our fundamental goal is really to build out the pipeline across all of those stages, because at the end of the day sales is a numbers game, and you want to ensure that you have enough people at the top of the funnel to feed those lower portions of the funnel, and to be able to better cater to those people’s needs as the interact with our brand. So, when they are coming to our product pages; when they are attending our events; when they are engaging with our content, we want to have a better understanding of where they are in the purchase cycle. Are they in the process of putting an RFP forth to other vendors? Can we get ahead of that curve, so that we are able to get in front of them before they release an RFP to the general public?

Squark is really what is allowing us to be more predictive – using a lot of data from a lot of disparate systems to enable us to understand who they are, what they like to see from a content perspective, and where they like to engage with us as a brand and as a product. And that helps us to optimize our media plan, optimize our media cross-channel strategy – whether it’s pay-per-click, or it’s owned email, or it’s what we are doing on nielsen.com. And optimize our audience segmentation so that we’re more effectively targeting the right people with the right content at the right time. And all of that, just like with B2C marketing, creates a better customer experience and enables us to more effectively and more inexpensively (most importantly) move people through that demand funnel, closer to that final, closed deal.

And AI is really what makes it faster; it makes it more accurate; and it helps us optimize our programs more effectively. Without that – and, believe me, I’ve worked in those circumstances without AI in the past – it’s a much more manual, much slower process, and your predictive capabilities are really diminished. Considering the amount of data; the amount of media inputs we see out there, it’s very difficult without putting your finger in the wind, to predict what’s going to be most effective in driving ROI for your marketing organization.

That brings me to this: We’ve implemented some of these predictive capabilities to get a baseline form which to develop our personas, our content, and our channel strategy. Now we really need to prove the impact of this – to measure the ROI and connect the dots between the media touch points – the engagement and conversion touchpoints that we are experiencing by distributing content across various channels – to our actual, marketing KPIs. As you saw in the earlier slide, those are connected to sales-accepted leads which, at the of the funnel, are closed deals and won revenue.

There is a wall between these two things. It’s very easy to track to the point of conversion – someone fills out a form; someone downloads something; someone attends an event – but to connect that to what’s going on in your CRM (and in our case we use Salesforce); what’s going on in your marketing automation platform (we use Pardot) is another challenge altogether. So you really need a system that’s able to integrate these various data sets and various platforms so that you can look at it holistically, and connect the dots between everything you are doing on the media and content side to how those things are impacting your most important KPIs that you present to the executive leadership.

So, this sort of summarizes it best. It’s how we attribute credit to the right channels and connect sales wins or conversions with tactics that is faster; that is more responsive; that is more intelligent than a small team would be able to do on its own. Even a large team with a large group of data scientists (and most marketing teams do not have the privilege of an army of data scientists) can’t really do things as fast as you need to be able to do them. To adjust creative. To adjust workflows in terms of they type of content we deliver and where we deliver it, based on consumer’s or customer’s past behavior or current behavior. All of those things need quick responsiveness, and AI is what helps us get to that point.

So, most importantly, Squark has really allowed us to quantify the marketing life-cycle. What I’ve depicted here, using the demand funnel I used in earlier slides, is some of the data inputs that are going into this decisioning engine, like marketing conversion rates, sales accepted rates, win rates, or average deal size. All of these things are helping this AI engine to be more predictive of sales and revenue and our core KPIs as a marketing organization.

The results have been phenomenal. And I attribute this to really the ability to automate a lot of the optimization that we need from both a personas perspective – who we’re targeting; a content perspective – what we’re targeting them with; and a channels perspective – what type of media, be it owned or earned, to engage with these consumers. By automating some of the decisioning around those three criteria, we’re better as a marketing organization. Better at predicting marketing qualified leads to sales qualified leads – the conversion point between those two. Which of our marketing leads are converting into the sales qualified category and why and how based on those three criteria? Which sales qualified leads are being accepted by the sales organization and followed up on for meetings? And finally, the ability to predict revenue, which serves obviously a few purposes – mainly our ability to forecast what we’re doing and the business results we’re, as a marketing organization, able to drive. This gives us buy-in with the sales organization. It gives us better integration with the sales organization for bringing these programs to life and closing the leads that we are generating. Without that, the system falls down from a B2B marketing perspective.

And finally, driving return on investment. So, we look at every dollar we spend across our paid media, across our content creation, etc. and we’ve seen, since implementing Squark, an 8X increase in our return on investment. So that ability to tell compelling and true stories about how your marketing investment and time is generating pipeline and revenue is essential for demand generation. That’s exactly what we are able to do more effectively with AI powering a lot of our decision making.”