how to leverage predictive analytics whitepaper for business

1 HOW TO LEVERAGE PREDICTIVE ANALYTICS IN B2B SALES AND MARKETING

HOW TO LEVERAGE PREDICTIVE ANALYTICS IN B2B SALES AND MARKETINGSay goodbye to spreadsheets and hello to predictive analytics


TABLE OF CONTENTSABOUT MARKETBRIDGE ................................................................................................................ 3

Part I: Why Predictive Analytics? ................................................................................................. 4

Part II: Applications of Predictive Analytics in Consumer Marketing .................................... 6

Part III: How to Leverage Predictive Analytics ........................................................................... 8

Part IV: A Practical Example of Predictive Analytics in B2B ................................................... 13

Part V: How to Build a Predictive Model ................................................................................... 17

Part VI: The Predictive Analytics Ecosystem ............................................................................ 22

CONCLUSION ................................................................................................................................ 23

ABOUT THE AUTHOR ................................................................................................................... 24

HOW TO LEVERAGE PREDICTIVE ANALYTICS IN B2B SALES AND MARKETINGSay goodbye to spreadsheets and hello to predictive analytics


ABOUT MARKETBRIDGE

MarketBridge is a leading technology enabled services firm, providing digital marketing, sales enablement, and customer analytics solutions for Fortune 1000 and emerging growth companies. We help companies improve sales productivity by increasing digital customer engagement and building robust customer analytics engines that focus marketing investments and sales activity on the right customers, with the right messaging and solutions, through the right marketing and sales channels.

Our unique RevenueEngines™ and SMART™ Analytics solutions deliver data-driven digital customer engagement by connecting marketing and sales to increase pipeline volume, velocity, close rates and customer loyalty. Our solutions are powered by best-of-breed technologies including social, marketing automation, CRM and business intelligence, all of which dramatically improve revenue performance, cost efficiency and customer experience.

Corporate Website:www.market-bridge.com

MarketBridge Community:www.the-digital-bridge.com

Phone:1-888-GO-TO-MKT

Corporate Headquarters:4800 Montgomery Lane, 5th Floor Bethesda, MD, 20814

San Francisco, CA49 Stevenson St., Suite 660San Francisco, CA 94105

New York, NY79 Madison Ave, 3rd FloorNew York, NY 10016


PART 1Why Predictive Analytics?

Predictive Analytics...Simply helping us more efficiently identify and harness patterns in our data


Marketers today are drowning in petabytes of data while sales teams are overwhelmed with leads and under-armed with tools that can actually make a difference, much less make a difference in the same quarter.

Marketing is doing their job by getting prospects into the funnel, while sales leaders are looking carefully at their pipeline and at the calendar. They need their army of sales reps to hit their targets in the next 90 days, and there aren’t that many things they can do. Hollering at marketing for more leads won’t help this quarter’s sales revenue, so they turn their attention to the bottom of the funnel and try to coach and cajole their reps into closing these deals as soon as is humanly possible.

But what if there were a way to prevent this bottom of the funnel hustle? There is indeed a powerful and often underused tool at our disposal.

Predictive analytics is a great tool to help us efficiently identify and harness patterns in our data. However, predictive analytics holds much more potential to transform organizations than simply ranking and ordering leads. When deployed correctly, predictive analytics can support marketing and enable sales by identifying better, more appropriate leads, thus improving the overall efficiency of both marketing and sales.

Although predictive analytics is the buzzword of the moment, it’s been around for a while. In fact, we encounter predictive analytics daily. In the next chapter we’ll reveal common examples of predictive analytics and then we’ll dive into how predictive analytics can be applied in B2B sales and marketing tactics.

PART 1: Why Predictive Analytics?


A most basic throw-back example of predictive analytics is the direct mail that lands in our mailbox every day.

Direct mail isn’t cheap, so marketers have long used very predictive data to justify the spend, typically drawing on demographic attributes like our geography, customer penetration in our market, marital status, income, credit rating and the value of our home. Even for shared mail, marketers use predictive statistics to drive decisions about whether they want to be in your mailbox or in your geography, at a given time.

While we might not appreciate the bountiful mail we get, we generally do appreciate the predictive analytics that fuel the product recommendations from consumer-centric giants like Amazon and Netflix (unless we’re spending too much of our hard-earned money because of them).

Marketers at these companies use highly predictive signals like prior purchase behavior, as well as product details like genre, year, and popularity of movies to make recommendations that drive consumers to spend more.

PART 2Applications of Predictive Analytics in Consumer Marketing


PART 2: Applications of Predictive Analytics in Consumer Marketing

What isn’t as widely recognized among marketers is how much predictive analytics drives decisions for email content. Many sophisticated marketers are using predictive analytics combined with online behavioral data like time on page, purchase recency, last product purchased and segment learning to optimize decisions around email content, products, and offers. These analytics can be tuned to optimize clicks or revenue, profit or value. (Also, it is critical that the tuning of the analytics and the evaluation of “success” leverage the same metrics).

Dell is a great example of a company using predictive analytics for email content. The company makes targeted product recommendations for consumers based on predictive analytics, in some cases machine-learning that gets smarter and smarter with every email click, every piece of content you download, and every minute you spend on a web page. Technology and automated processes are playing a big role here as many marketers have a large number of email communications, both scheduled and triggered,

whose effectiveness can be maximized with the right content decisions. These decisions are also ideally drawing on site behavior and, as such, require a level of timeliness best achieved through automated processes to define and apply predictive analytics.

A lesser known application of predictive analytics is targeted display media. Predictive analytics allows marketers to capture data and make real-time decisions about targeted ad placement, based on your online behavior. Marketers are leveraging contextual data to score algorithms in real-time and make bidding decisions about whether to serve up an ad along with which complimentary ad, which content, or which offer. Attributes like which browser we are using, the time of day, where we live and which type of device we’re using are commonly drawn upon to optimize targeting decisions for online media.


Taking off our consumer hat and putting on our marketing hat, predictive analytics helps us as B2B marketers to make a variety of decision types more effectively. We organize these into three primary categories: who, what, and where.

Predictive analytics are most commonly applied to decisions about “who” based on various leading indicators, drawing on data patterns to understand and harness predictive signals that identify who is likely to engage, buy, convert, or have a high lifetime value.

As we move from the who, we look to predictive analytics to determine the next best product that we want to serve up. Is there a right offer, and if so what is the message we want to use to deliver that product and offer?

Analytics can also help us optimize where to engage. It can help identify the right outbound media, whether its mass media or precision media like email or direct mail, sales call (field or inside) and increasingly print and display. Inbound channel propensity is the final key decision point that often draws on predictive analytics.

• Do we want to drive customers to a website? • Are we asking them to call us or request a face-to-face meeting?

These are the most common signals that B2B marketers are using predictive analytics to make decisions around, and the impact of using predictive data efficiently can be immediate and profound.

To understand where B2B organizations are most often leveraging predictive analytics in an ROI positive way, let’s look at a classic marketing and sales funnel. From Reach at the top of the funnel, all the way down through Engage, Convert, and Expand.

PART 3How to Leverage Predictive Analytics


PART 3: How to Leverage Predictive Analytics

Predictive analytics is most commonly used to identify the best targets and reach them where they’re already engaging and buying.

• What was the hook or offer that compelled them to engage? • How are they engaging? • What content drives them to engage?

Here is where we often clone our best customers and find them in the marketplace. Generally we’re drawing on firmographics of current customers—things like revenue, employee count, number of locations, credit rating, and industry.

We use these firmographics as a bridge across both existing customers and the pools of prospects we wish to communicate with, and these attributes serve as the medium through which we apply predictive analytics to make better decisions about how we’re going to reach out to them, whether it’s by phone, mail, email or other digital engagement channel.

Reach

Industry

Revenue

Employees

Credit Rating


Our most successful B2B clients are leveraging predictive signals and data to prioritize their leads and optimize inside sales time by nurturing the right leads (who), in the right channels (where), with the right content and tactics (what).

Many B2B marketers and sales teams are overrun by the sheer volume of leads and are not spending call time in an optimal way. More importantly, they’re not leveraging digital engagement to fill the gap. Predictive analytics can help prioritize your call list and understand:

• Who is ready to engage with us? • What is their channel preference? • What signals has this prospect given us that tells us he or she is likely to engage? • Is this the right lead for our sales force to contact?

Once we know the prospect is ready to engage, we can then also predict which messages to which a prospect is most likely to respond favorably. Marketing Automation platforms collect an abundance of data on campaigns and marketers often run tests of different messages to see which work best. This feedback loop allows us to further optimize our tactics to ensure our prospects engage.

It’s critical that marketers capture and draw on this predictive data to understand when and how sales and marketing should reach back out to prospects. Business buyers are smarter and more educated about your products than ever before, and predictive analytics will help you meet customers on their terms, wherever they are on their buyer’s journey.


Engage

Channel

Firm Size

Engagement RecencyPromotion


As we move further down the funnel into conversion, we’re leveraging data signals and predictive analytics to close business with the optimal mix of channel, product, offer, and message.

The most predictive indicators for closing the sale are often focused on content marketing. How do we get the right content in front of leads, and as we engage them, how to make sure we’re highlighting the products that interest them—so we can get that deal closed now? We’re looking at things like where leads are engaging and any historic purchase activity if it exists.

Increasingly important to marketers is integrating what we know about a lead offline with web log data, particularly where leads are authenticating. Are they consuming gated content? How much time are they spending on a page? Ideally, we want to tie the different product offerings they’re viewing online to their personally-identifiable or firm-identifiable information.

If marketing and sales is looking to sell an add-on or a cross-sell, understanding a customer’s last purchase and other RFM (recency, frequency, monetary value) characteristics of purchase and browsing behavior is essential.


Convert

Last Purchase Email Clicks

Lead Source


For most marketers, the ultimate goal is to find, nurture, and expand customer relationships so that they are as valuable as possible. As our customers move further into the funnel, their value naturally grows as does our accuracy in predicting their lifetime value. As customers move through the funnel, the richness of data from which we can identify patterns expands into purchase and engagement activity both on and offline.

Once a prospect becomes a customer, we often have rich data on how they interact with our products (especially if our product is software!) which provides us signals to when they’re most likely to upgrade, products that we should cross-sell to them and also when they’re at risk of leaving. When you fold in service and other engagement data beyond sales and marketing (for example calls to customer support or service) our ability to predict when customers are at risk becomes even stronger.

Effective lifetime value (LTV) predictions focus on two key dimensions: your tenure as a customer and the volume of spend or profit across each time period of your ‘survival’. These are born from attributes such as how much a customer spends on a first purchase, their purchase frequency, how they pay, whether they are adopting your solution or products, if they are engaged in your web properties, and so on.

Predictive analytics allow us to project a customer’s LTV and toggle sales, marketing and service spend accordingly to create a competitive advantage.


Expand

Usage

Purchase Amount

Payment Method


PART 4A Practical Example of Predictive Analytics in B2B

Here’s an example of how a B2B organization can leverage predictive analytics to find their perfect customer. Our mock company, ACME Applications, sells hardware and software to small and medium-sized businesses. Based on ACME’s offering, current customer profile, and whom its offer is resonating with, ACME’s perfect customer is Agile Mobile.

If we’re marketers at ACME and want to find more customers like Agile Mobile, we first want to see what our prospect universe looks like. We have plenty of top-of-the funnel firmographics and characteristics at our disposal.

ACCOUNT AGILE MOBILE, EST. 2011 OWNER: ELENA STARK LOCATION: SAN MATEO, CA AND BANGALORE, INDIA EMPLOYEES: 34 INDUSTRY: PROFESSIONAL, SCIENTIFIC, AND TECHNICAL SERVICES (54) REVENUES: $10.5 CREDIT RATING: A Agile Mobile opened their doors just over three years ago. They specialize in mobile applicaYon development. Run by Elena Stark, the business has grown reliably and steadily over Yme. With good margins and a great financial record their credit raYng is strong. Like others in their industry, Elena is looking forward to a strong future of growth. Example Only: Not a Real Company


As we mine the data, we start to find our ideal range, our customer sweet spot. We’re successful with small firms with 10-49 employees in the professional services industry who have been in business for less than 7 years. Our best customers have small to medium square footage, good credit or no credit history, and revenue of $10-60 million.

Now we need to move from understanding who our perfect customer is to planning an outbound marketing campaign or building out a target list for inside sales. We start by trying to understand how many firms out there fit these categories.

PART 4: A Practical Example of Predictive Analytics in B2B

Employees Target Industry Time in Business Sq. Footage Credit Rating Revenues

Firms with 10 to 49

employees

Professional services

Younger businesses

Small to medium square

footage

High credit …

Or no credit history Revenues

$10- $60 MM


As we move from having an overarching view of the ideal customer into the spreadsheet view as we start to plan our campaign, it might look like this.

As you can see below, we have a scalability issue. In this example, given the criteria, when we combine all the data together there are only 8,448 total firms that meet all of our criteria. This means we don’t have a huge population to go out and market against. In addition, we have to ask ourselves, are these really the best targets?

Are there more intelligent ways to think about this not only to improve our scale, but to differentiate the very best prospects and opportunities from those that might be less viable for us?


Waterfall counts … TBD

Employees Industry Age

<10 & Unknown 11,463,543 Public & Non-‐Profits 1,480,867 0-‐2 years 3,324,670

10-‐49 2,040,705 Private -‐ Goods 5,985,910 3-‐5 years 3,509,974

>49 494,374 Private -‐ Services 6,531,845 >5 years 7,163,978 15% 47% 25%

Sq. Footage Credit Ra:ng Revenue

<2.5k & unknown 5,412,622 Unknown 2,965,757 <$10MM 2,213,793

2.5k-‐10k 5,780,245 <A 8,547,121 $10-‐60MM 856,634

>10k 2,805,755 A+ & A 2,485,744 >$60MM 10,928,195 41% 39% 6%



Also, with the old spreadsheet approach, what if a prospect checks boxes for a couple of attributes but is missing one or two? Are they a viable target or not? What if a lead is a great fit in one attribute but lacking in another?

The challenge for the spreadsheet method is these factors as well as the complexity of relationships. Not all attributes are equally weighted, and they’re not necessarily equally good or bad. We have to think of these complex relationships in both multivariate and single-variable (or univariate) ways. Where our customer sweet spot lies may not be as cut-and-dry as we think.

For example, if we’re looking at two groups, our prospect universe and

our current customer base, to compare one attribute, say a firm’s average time in business, we might see that for our prospect universe it’s 12 years and for our customer base it’s three years. A logical conclusion to draw is that younger businesses are better. But if you dig in, you might see a skewed bell-shaped curve. The real sweet spot where we have the best representation of customers isn’t the absolutely youngest businesses, but those who have been in business three to six years.

As we do initial exploratory analysis in preparation for building a model, these are the types of relationships we’ll uncover and harness to make sure our targets are the best they can be.

Now it’s time to start building our model.

yrs

Lacking "fit incertain factors

RelativeImportance

Complexity of Relationship

Time in business 1 year 20 yrs

Pro

pens

ity to

con

vert

Low

H

igh

Prospect universe Customers

Group

12 years

Average time in business

3 years

So, younger businesses are better…. Right?

Sort of


PART 5How to Build a Predictive Model (It’s easier than you think.)

In the example of ACME applications, we’re beginning to understand who our target is. We’re convinced that predictive analytics is a better way, so we’re ready to build a model to create a top of funnel marketing campaign. At MarketBridge, we leverage a standard process with our B2B clients to frame out the business problem all the way through collecting and analyzing data, delivering insights, and finally taking action on them.

Building a model is easier than you think. First you frame the problem or objective. In this case, it’s “find prospects who look like and will spend like my current customers to target them with a marketing impression.”

Next we collect data for this set of customers along with a set of prospects where we’re going to go fish. We need to ensure a consistent set of attributes across the two data sets, for example, firmographics. Then we analyze, organize and cleanse the data—and any analyst will tell you this is the bulk of the work. We need to identify which attributes are truly related to being an ACME customer and how those attributes relate to one another. Once we’ve identified these attributes, we build the model.

Now it’s time for a little math. We promise it won’t hurt.

Frame Collect Analyze Deliver Act


PART 5: How to Build a Predictive Model

Building the initial model and identifying that equation is a relatively short-term project. You build it and it’s done. How you apply it requires more ongoing use. Some marketers feel a little intimidated by the idea of using and scoring the model on a regular basis, but we can break it down into simple math (it’s easy!).

Below is an example of an equation we would use to predict spend (using linear regression) with each of the important parts of the equation broken down.

The “dependent variable” … or trait you want to identify – in our case it’s customer spend

The “coefficient” – think of this as the “weight” that is applied to the independent variable

An “independent variable” … or trait that relates to your end goal (usually you will have many different independent or “predictor” variables in an equation)

y = a + β x + e



Now let’s plug in some numbers to predict customer spend. In the example below, the weight of 2.9 means that for every increase of one employee, the firm will spend 2.9 thousand more dollars.

A slightly more complex (and more realistic) example is below. In this example when trying to predict spend, think of it in a simplistic way: more employees is very good, a bigger footprint is pretty good, and a bad credit rating is just that—bad.


The weight we apply to the employee count variable

The predictor variable that is highly related to customer spend

Customer Spend = 4 + 2.9 (# of employees) (in thousands)


The weights we apply to each predictor variable

The predictor variables that are highly related to customer spend

Customer Spend = 2 + 2.5 (# of employees) (in thousands)

+ 0.003 (square footage)

- 1.6 (credit rating of "C")



If we carry this through we get a predicted spend of $102,000 for Agile Mobile. Knowing this, Acme Applications can put an appropriate amount of spend against Agile Mobile as it relates to outbound sales and marketing touches.

If we as marketers at ACME apply this basic math equation for predicting customer spend to every account, it’s clear how we can build a cleanly ranked and ordered list, bundle that rank order into higher level groups, and suddenly we’ve harnessed the power of predictive analytics at scale.



The predictor variables that are highly related to customer spend

$102,000 = 2 + 2.5 (34)

+ 0.003 (5,000)

- 1.6 (0)




There are a number of approaches to building the equation on the previous page. Some common statistical tools and methodologies are below. Once we’ve built the model, we’ll need to evaluate it to make sure it’s a good fit. Testing is key when building an analytical model.

Lastly, we need to take the data generated from our predictive model and make sure we can execute on it in our marketing campaign or sales play. From a delivery perspective, there are two key pieces. First, we want to deliver business-relevant insights about the model. Not many marketers or sales teams are comfortable ranking their call list based on a black-box type of model. Organize the information to clearly identify who the model says is a good customer and who is not. Does it make good business sense?

Perhaps most important, we want to deliver the actual targets, whether they are consumed by marketing automation, campaign management, CRM, or another tool. As with all technology-enabled marketing, the end-goal of predictive analytics is to have a positive effect on our bottom line, so a plan and process for execution is critical to our success.

Logistic regression to predict a binary outcome (0/1)

Survival analysis coupled with revenue/margin estimation

Logistic regression if binary or linear regression to predict spend level (continuous)

Market Basket analysis to identify associations between products or product propensity modeling using logistic, decision trees, or neural network models Segmentation driven using techniques like k-means clustering, latent class, factor analysis, discriminant analysis

Media and channel propensity modeling using decision tree, or logistic regression Survival analysis or logistic

regression

Logistic regression to predict a binary outcome (0/1)

Logistic regression if binary or linear regression to predict spend level (continuous)

Survival analysis coupled with revenue/margin estimation

Survival analysis or logistic regression

Market Basket analysis to identify associations between products or product propensity modeling using logistic, decision trees, or neural network models

Segmentation driven using techniques like k-means clustering, latent class, factor analysis, discriminant analysis

Media and channel propensity modeling using decision tree, or logistic regression


Moving beyond the ability to build a predictive model, there is a broader predictive analytics ecosystem that is important to generate value.

Data

We’ve all heard this said about data, “Garbage in. Garbage out.” For data scientists, the data fueling the predictive analytics ecosystem is the air we breathe.

Clean, well-organized data is great, but not so perfectly organized data doesn’t need to constrain our ability to leverage predictive analytics. At MarketBridge, we talk to many clients who have pain around this issue of data organization. It’s a huge challenge. But we can typically get more out of our data than we think. In addition, there are a plethora of third party sources we can use both to source top-of-the-funnel opportunities and to enrich, cleanse, and update the data we already have.

Systems

The systems we use are the foundation we’re building everything on. We have to deliver predictive analytics into something. The data has to live somewhere, in a marketing automation, campaign management, or CRM system. The ability for your system to consume predictive analytics is all-important. Some tools have the ability to score and potentially to build analytics in an embedded way, but if not, it’s very easy to get analytics into these systems through loading of flat files or SaaS plug-ins.

Effect

Having an effect in market requires that we take action on the insights and outputs of our predictive analytics. A classic example is attrition modeling. It’s one thing to identify who is likely to attrite and another thing entirely to deploy a program to use those predictions to stem attrition. The predictive analytics and approach you use to drive the intended effect need to work together.

PART 6The Predictive Analytics Ecosystem


CONCLUSIONSo, get started on leveraging predictive analytics. Your competitors may already be there, but even if they are there it’s time to surpass them with better end to end use and application of predictive analytics. As you get more comfortable with the B2B uses as outlined in this whitepaper you can start to pave the way around the more progressive digital applications.

Lastly, while there are great tools out there to put predictive analytics in the hands of a business user, if you haven’t done it before please consider hiring a professional. There are a number of pitfalls in data handling and predictive modeling that you’ll be well armed to avoid to ensure the best outcome. Ideally they can also help guide the end to end process: frame the problem (one you can action on effectively), organize your data, build a strong model, and deploy and act on it.


ABOUT THE AUTHORS

Stephanie Russell // SVP, Business Analytics

Stephanie leads the Business Analytics practice for MarketBridge. With over 15 years of experience in CRM strategy, analytics, and enabling technology, Stephanie leads a team of experts across analytics and enabling technology disciplines. Through strong client partnerships, the team delivers relevant, effective and actionable analytic solutions across industries for clients such as HP, Staples, Humana, and others. These solutions inform both online and offline sales and marketing decisions and range from predictive modeling and segmentation solutions to web analytics and closed loop sales and marketing effectiveness measurement.

Prior to her role at MarketBridge, Stephanie focused on CRM analytic strategy, solution development and delivery at Merkle. This included technology and analytic solutions such as agile BI deployments, media mix modeling, media attribution, lifetime value modeling, experimental design, and program analytics for clients such as Dell, MetLife, Under Armour, Disney, DirecTV, Bristol-Myers Squibb, Ally Bank, GEICO and others.

Stephanie holds a Bachelor of Arts degree in Economics, with a concentration in Econometrics, from William Smith College and an MBA, with a concentration in Marketing, from American University.

Peter Guy // SVP, Products

Peter leads MarketBridge’s product strategy and technology development to address the rapidly changing sales and marketing challenges of our clients. Peter drives MarketBridge’s RevenueEngines™ digital customer engagement and S.M.A.R.T.™ Analytics solutions that accelerate revenue growth and end-to-end marketing and sales productivity for Fortune 1000 clients. As companies make significant investments in sales and marketing software – CRM, marketing automation, social media, business intelligence – MarketBridge’s solutions connect these products into scalable and coherent “digital + data” strategies and programs.

Prior to MarketBridge, Peter was Managing Vice President, Product Marketing at Gartner. Before Gartner he was Vice President at Lattice Engines where he led customer development of their predictive analytics solution for B2B sales productivity delivering nearly a billion dollars of incremental revenue for customers such as Dell, Staples, EMC, vmWare, HP, ADP and American Express.

Peter has a BSc in Computing Science and a BEng in Electrical Engineering from Dalhousie University (Canada) as well as an MBA from INSEAD (France and Singapore).

how to leverage predictive analytics whitepaper for business

Data & Analytics

predictive analytics

b2b sales

customer analytics solutions

predictive analytics2

predictive analytics3

predictive model

smart analytics solutions

sales enablement