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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.”

Can AI Help Retail Store Salespeople Sell More?

The best retail sales people excel because they attract customers, serve them well, and keep them coming back. They understand individual tastes and show complementary styles and accessories, boosting sales volume and customer satisfaction at the same time. Underlying this ability to perform on the floor are memorization of inventory, diligent work on outreach, and constant networking to expand clientele. You are probably thinking of your favorite professional right now—the one who can sell an entire ensemble to a long-time customer while helping two walk-ins miraculously “find” items they love.

AI is not going to replace people like these. It will augment their abilities to sell even more, and it will help salespeople with lesser skill to be more like them. Here’s how.

Get Them In
AI in the back office will enable ever-more-personalized messages to be sent to prospects and customers with compelling reasons to make a store visit. Machine learning allows buying patterns and trends to be predictions, as opposed to reactions. Without programming, AI algorithms can be tapped to show affinities and cross-sell opportunities with startling accuracy. A personal note from a with exciting content sent (automatically) from a sales professional is much more likely to prompt a visit than a generalized message. Salespeople get the advantage of outreach without consuming selling or personal time.

Treat Them Right
In-store selling tools can take advantage of AI in real time to offer suggestions on colors, patterns, compatible accessories, and sell-along items with knowledge of available inventory. Attractive assortments are simple to present to the buyer. This increases sales of available SKUs and reduces the time spent per visit—with better customer satisfaction. That is a recipe for better commissions.

Bring Them Back
Re-engagement cements the value of past, pleasant shopping experiences. AI makes it simpler not only to remind customers of items they’ve seen, but also suggest new merchandise choices. Here is where the predictive abilities of machine learning can amplify the seeming clairvoyance of top sellers and add some of that talent for new salespeople. By focusing correctly on sales by a person, to a person, AI for in-store retail can be effective while avoiding the “creepy” factor of behavioral online ads.

Make It Exciting
AI continues to help differentiate in-store retail experiences from online shopping. Real time, internet-of-things, augmented reality—driven by machine learning—is already being incorporated on the sales floor. But salespeople and inventory are the core reasons that stores draw traffic. Ironically, the primary benefit of Artificial Intelligence for retail stores will be helping real people sell, in person, to their customers.

Consider the Future
AI can create a new world for in-store retail that brings together machine intelligence with human intelligence to make the AI-augmented salesperson a reality. Additionally, grand orchestrations of AI-powered and data-informed store operations and shopper experiences are possible. Winners will be the retailers who conjoin the best of AI with the irreplaceable human elements shoppers will always seek.

Why Marketers Need Automated Machine Learning

We can imagine that marketing before computers was primarily based on intuition. Sure, managers had ledgers that allowed them to see sales lift from advertising campaigns, but without insights about which customers were responding to which programs. (We’ll spare you another recitation of the John Wanamaker quote.) High-value customers were known through personal relationships, but marketing and selling costs could not be tracked very well by individual or account. Manual accounting tabulation meant slow reporting. Successful marketers were those who could “feel” what worked based on this restricted set of information.

How far we’ve come.
Here we are, with widespread use of CRM and marketing automation systems tracking business with phenomenal granularity. We know more about consumer behaviors and customer lifetime value that was imaginable only a few years ago. Advertising is measured and viewed instantly with explicit response tracking and social media profiling. Segmentation is fast approaching single-member entities for targeting and personalization. We can see statistics in reports in near real-time. Filters, consolidations, charts, and dashboards provide an exquisite view using the rear-view mirror of history.

Yet successful marketers are still those who can “feel” what works. Contemporary marketing systems are heavy on the past and light on prediction.

Faced with hundreds or thousands of tags and attributes, conventional methods are ill-suited to deciphering trends and motivations and applying them to prospect and customer databases to produce real predictions. It is just too difficult to code Boolean-logic programs to produce predictive analytics in a timely manner. The promise of accurate marketing attribution to guide the future remains largely unfulfilled.

Human intuition is still the primary way marketers transform data insights into actions.

Until now, that is…
Automated Machine learning (AutoML) is a revolution in predictive accuracy and speed that delivers what marketers need. AutoML is a technique that enables systems to provide answers without explicit programming. Using artificial intelligence (AI) algorithms, AutoML learns from training data sets such as won-lost or lead conversion reports. This “supervised learning” is doing exactly what smart people would do if they had months to work at it with a team of analysts and programmers. It just happens in minutes with no development project. When models are applied to full prospect and customer databases, startlingly precise predictions are generated.

It isn’t magic. AutoML is just automation of AI computational breakthroughs that can find the patterns and associations that have always been present in your data. AutoML gives marketers their first, real tool for augmenting intuition with data science. Finally, we have ways to apply imagination with confidence. AutoML frees marketers to do what they do best; what they really want to do in place of system wrangling. Marketer are free to market.

  • Predict which customers are likely to churn so you can target them with retention programs.
  • Rank offers by predicted likelihood to convert for each prospect and send the best.
  • Forecast which leads will produce deals the highest customer lifetime value.
  • Identify cross-sell/up-sell opportunities that will boost revenue.
  • Offer account based marketing content that actually accelerates customer journeys.

For the first time, predictions that you’ve always sought are accessible without coding. Your world just changed.  Find a way to jump into the revolution. Ask Squark for a free AutoML assessment.