The DNA of your NexT cusTomersPredictive Lead GenerationPredictive Lead Scoring

A buying guide from experts to:

Understand the emerging Predictive market

Evaluate vendors In North America and Europe

Master common use cases & scenarios

Though they are often associated with pure lead generation, Predictive Analytics solutions are not limited to identifying new prospects, but can in fact be used at different stages of the sales cycle. When applied to new leads, these technologies can help gauge buyer intent.

Existing leads can likewise be evaluated according to their propensity to convert, and the outcomes of existing opportunities can also be assessed. Finally, additional scoring scenarios are possible according to the configuration of the technology (see below).

The market for B2B Predictive Analytics SaaS technologies has enjoyed uncontested success in recent years. Many early adopters have testified to the value of these innovative solutions, lauding their ability to forecast future business successes based on existing data patterns.

Predictive Analytics for marketing and sales

Considered a subcategory of Predictive Analytics, Predictive Lead Generation, which is dedicated to scoring new leads, along with Lead Scoring, which focuses on scoring inbound and existing leads, are the most commonly used types of solutions. Essentially, Predictive Lead Generation is used as a capability enhancement technology for marketers looking to drive conversion and build a steady and predictable sales funnel.

Outbound prospecting, campaigns, account-based marketing

Inbound leads, demand generation programs

Opportunities, pipeline forecasting

Proposals, pricing

Upsell, cross-sell, churn

Predictive lead generation and predictive lead scoring

Predictive lead scoring

Predictive opportunity scoring

Predictive opportunity scoring

Predictive lead scoring

Market dynamics in North America and EuropeAdoption Rates and Existing Customer BasePredictive solutions are enjoying a rapid adoption rate around the globe from a variety of customers. Unsurprisingly, the IT industry is spearheading the use of Predictive Analytics, with an adoption rate of 78%1. The overall existing customer base includes large,

multinational corporations (Amex, Oracle, La Poste, Dell, GE, Microsoft, BT, Lloyds Bank, IBM, Adobe, among others), as well as small,up-and-coming entities such as Docusign, Marketo, Optimizely, Tableau, AdRoll, Box, and Zendesk. This would seem

to indicate that company size is not a barrier to adoption. In fact, SMBs have embraced predictive lead scoring much faster than large companies, with 17%2 of B2B SMBs ($250m to $1B in revenues) already using predictive lead scoring.

“When considering different solutions, Predictive Lead Generation for prospecting makes sense for SMBs who may not have largescale lead generation programs in place. On the other hand,bigger organizations with a steadier lead stream can use predictive analytics to refine existing data, helping prioritize sales and marketing activities all along the funnel.”

David Raab, consultant in marketing technology evaluation and analytics.

The good news for Predictive Analytics suppliers is that the market is far from saturated: according to influential research firm TOPO, 37%3 of high growth companies plan to invest in predictive analytics over the next 12 months.

At publication of this ebook, we estimate that roughly 800 companies are currently using Predictive Analytics for marketing and sales purposes in North America and Europe. Indeed, some companies actually implement multiple solutions in order to cover different geographic areas. This overlap is sometimes necessary since Predictive Analytics providers are limited by the regions covered by the data providers they work with.

1 Sirius Decisions -Predictive Lead Scoring study - (2014)2 Sirius Decisions -Predictive Lead Scoring study - (2014)3 TOPO -B2B Predictive Analytics Technology Report - (2015)

Predictive Lead Generation at a glance

Predictive Lead Generation solutions identify and score leads by buying intent probability. The technology relies on past sales successes, business environment and extensive external data to qualify the buying propensity of new leads on a given market. This allows the prioritization of marketing and sales actions relating to higher-conversion and higher-velocity leads.

Differences between North America and EuropeThe market for Predictive Analytics applications for B2B sales and marketing professionals differs slightly between North America and Europe. The majority of American vendors, such as Infer, Lattice Engines, 6sense, and Fliptop, have chosen to build their offer around scoring inbound or existing leads and opportunities, complimenting a strong culture of inbound marketing in the States.

European players like IKO System are instead more likely to position themselves as an outbound prospecting or Account-Based Marketing solution, focusing on scoring net new leads.

Vendors in Europe typically target the top of the sales funnel, addressing the need to generate new prospects that yield higher conversion rates than traditional outbound leads.

SiriusDecisions explains the disparity between European and American vendors:

“Most vendors in the North American market have spread to a broader part of the sales funnel. This can be attributed to the revival of outbound marketing in the U.S. that until now had been largely ignored due to a strong inbound culture.”

Kerry Cunningham, research director at SiriusDecisions

The WHY: specific predictive scenariosWe have seen that predictive technology can be applied at different stages of the funnel, and can address different business-related goals. Although American and European vendors have made an effort to differentiate their respective offers, recent surveys show that marketers’ chief business concerns are practically equivalent on both sides of the Atlantic:

Below, we will detail common use cases for Predictive Analytics that address these concerns, including:

• Building an outbound machine with Predictive Lead Generation• Uncover the buying probability of inbound or existing CRM leads• Build an Account-Based Marketing process• Score opportunities’ potential, forecast revenues• Other applications

Top Marketing priorities: North America vs EMEA

30

20

10

0Increase the

number of leads

Predictive lead generation

Convert leads to customers

Predictive oppty scoring

Reach relevant audience

Predictive lead scoring

Upsell and cross-sell

Source: Hubspot State of Inbound 2014

Scenario 1: Build an outbound engine with Predictive Lead GenerationDefining an optimal demand generation strategy often boils down to determiningthe most effective mix of inbound and outbound marketing techniques toimplement. While most companies have favored inbound tactics over the past few years, its inherent limits - issues like content resources, growth inertia, and scalabilty - have prompted businesses to start exploring new outbound prospecting strategies. I dicative of this new orientation is the fact that leadingnames on the

inbound marketing scene, such as Hubspot, Adobe, Marketo, Oracle/Eloqua and others, have all turned to Predictive Lead Generation as a main resource. Thesetechnologies are put in place primarily to fortify outboundteams charged with ensuring the acquisi-tion of new customers and prospecting large, complex accounts.

Facilitating this recent interest in outbound are the myriad of new tactics in sales automation, social

selling, and inbound-out-bound aggregated intelligence. These tools increase outbound engagement metrics and lead conversions, making iteasier to track progress and ROI.

The acquisition of Fliptop by Linkedin in August 2015 confirms that there isindeed a strong market need for leads based on predictive analytics, as opposed to more traditional “database leads” filtered merely by job title or other similar criteria.

“Predictive Analytics brings the most value at the top of the funnel, before Sales Reps have a relationship with prospects. Finding the best leads is important, but engaging these mature leads before your competitors do is critical.”

Matt Heinz, president of Heinz Marketing Inc

How does it work?Predictive Lead Generation is changing the face of outbound marketing by scoring the buying probability of all potential leads on the market for their customers’ offer.

By identifying dynamic patterns - also known as buying signals - from existing customers (i.e. past successes), the Predictive Lead Generation vendors create the DNA signature of the ideal customer profile (ICP) and scan all potential leads on the market to score their buying propensity.

Predictive Lead Generation solutions can also usually provide new leads from a list of named accounts, in which the territory is delimited, or from a dynamic territory, using criteria such as demographics and firmographics.

Challenges addressedPredictive Lead Generation helps marketers address strategic and tactical goals:

• How do I identify new leads with a high probability of converting?• How can I create a streamlined system to identify, engage and qualify a given number of new leads per day in a routine and scalable way?• How do I build a reliable and predictable sales funnel?• How can I improve my lead-to-opportunity conversion rate?• What strategy can I put in place to rapidly attack green fields (i.e. new markets)?• How can I improve department efficiency so that the sales team can fully focus on worthwhile leads?

Predictive benefitsPredictive Lead Generation provides a flow of net new leads ranked by buying probability, along with significant signals or patterns relevant to that lead. Some vendors provide additional capabilities useful for lower down the sales funnel that allow prospectors to automatically engage with leads via email workflows or ad retargeting. They might also allow prospectors to measure the effectiveness of new leads provided by predictive analytics by evaluating conversion rates or velocity in the sales cycle.

Scenario 2: Uncover the buying probability of inbound or existing CRM leadsBy matching leads with a given ICP (Ideal Customer Profile), Predictive Lead Scoring solutions can also evaluate the buying probability of incoming or existing leads in a client’s CRM. In this scenario, vendors rely not only on demographics, firmographics and external lead or company data, but also intensively analyze the past CRM activity of each lead, including lead source, campaigns and activities. The more criteria there is to analyze, the better the modeling and scoring becomes. Behavioral data from marketing automation sources can also be incorporated into the predictive model if such technology is in place.

First, custom fields are created in the CRM or marketing automation platform to display lead scores. Lead routing rules are then established to helps sales and marketing departments better prioritize lead engagement, attribute prospects toeach sales rep, build

custom reports, or run specific campaigns for mature or high-value dormant leads. Predictive Lead Scoring can also be leveraged to measure the success of marketing actions (sources/cam-paigns/programs) byevaluating the quality of the leads they produce.

Some predictive vendors also provide ‘scoring reasons’, or insights that explain why a lead is given the score it has been attributed. They may also append CRM fields with relevant lead or account data missing in the client’s CRM from their own sources.

Challenges addressedPredictive Lead scoring addresses the following questions often posed by marketers:

• Which dormant leads in my CRM are worth warming up right now?• How do I prioritize incoming leads? How do I establish incoming routing?• How do I encourage my sales reps to accept and contact leads?• How do I segment my contact database to generate the best leads?

Predictive benefits

Jon Miller, co-founder of Marketo and now CEO of Engagio, defines ABM as, “Intentional go-to-market activities that coordinate personalized marketing and sales efforts to open doors and deepen engagement at a specific account.” Essentially, ABM aims to allow marketing and sales departments to align and focus their efforts to target a limited number of key accounts.

Many new technology layers and organizational processes have recently emerged to provide customers with a consistent Account-Based Marketing strategy. TOPO deftly summarizes how Predictive Analytics can be applied to an ABM Strategy using the following five steps4:

Scenario 3: Build an account-based marketing process

”Intentional go-to-market activities that coordinate personalized marketing and sales efforts to open doors and deepen engagement at a specific account.”

4 http://blog.topohq.com/the-account-based-marketing-technology-stack-emerges/

• Build a list of target accounts using Predictive Analytics• List the best contacts into these accounts with Predictive Lead Generation• Engage leads with a personalized approach: either through email workflows or targeted advertising (some predictive vendors provide solutions and third party integrations)• Personalize the website experience for targeted accounts• Manage the CRM with an account-focused approach (for example by linking leads to accounts in SFDC or analyzing the CRM metrics by accounts)

Challenges addressedPredictive Lead Generation addresses the following challenges inherent in ABM:

• How to uncover a list of high-potential accounts to target for a specific offering?• How to find the best contacts to address in specific accounts?

As long as an agreed-upon ICP exists, Predictive Lead Generation vendors canevaluate the potential of all addressable accounts on a market and rank them bybuying probability. This strategy yields much better results than simply guessing which accounts

will be the most fruitful based on static lists. In most cases, one predictive model is used per product or per offer, although occasionally individual models can be created per sales territory. Once a territory has been

established and if the model has been configured tosurface individual leads, the predictive solution will score the buying propensity of each contact - in all likelihood a decision maker - within each targeted account.

Predictive benefits

Trying to forecast how many open opportunities will close within a giventimeframe has always been a challenge for VP Sales professionals. Similarly, sales reps are also constantly asked to prioritize their prospecting efforts based on which leads seem more promising, a task which is not always easy to accomplish due to lack of relevant data. Predictive Opportunity Scoring vendors can step in to help solve these problems, and can also often predict close rate, deal size, and close date.

As opposed to scenarios 1 through 3, predictive models based principally on the ICP are not sufficient here to evaluate the propensity of winning an opportunity. Predictive Opportunity Scoring instead leverages data other than demographics, firmographics or corporate buying patterns, such as the sales actions, on-going deal momentum and the structure of an opportunity (price/terms/product...).

Scenario 4: Score opportunities’ potential, forecast revenues

Predictive Opportunity Scoring solutions rank each opportunity by its propensity to close, at an expected amount and at a certain time. VPs of Sales build reports from these predictions, (though some reports are automatically created by thesolution), to identify the lists of opportunities at risk (low score), to evaluate the total bookings expected for a period, and to identify opportunities where ‘money is left on the table’.

Challenges addressedPredictive Opportunity Scoring solutions address these questions:

• What is the quality of the pipeline?• How much revenue will we close this quarter?• Which of the current opportunities require additional attention from sales reps?• Are there open opportunities which could pay more than we anticipate?• Which sales-forecasted opportunities are at risk?

Some predictive vendors focus on other parts of the funnel. For example, they might predict the success of marketing programs, allow one to optimize pricing, predict renewal/churn potential of recurring customers, or predict upsell/crosssell opportunities in certain accounts.

Essentially, the technology that supports predictive analytics can be applied to any consistent dataset of high quality. One vendor (InsideSales.com) even uses predictive technology to evaluate the potential productivity or performance of tobe-recruited salespeople by comparing the candidate’s ‘skill DNA’ against a model of existing top sales performers.

Other scenarios

5 Sirius Decisions -Predictive Lead Scoring study- (2014)

Benefits seen by current users5

90%of users say that predictive lead scoring provides more value than traditional lead scoring.

98%of current users of Predictive Lead Scoring say they would purchase again.

250%average ROI from implementing predictive lead scoring.

Predictive benefits

Acquiring internal data

Predictive Analytics in action: How does it work?

Typically, predictive vendors will first build the ICP model that will be used to evaluate the potential success of leads or opportunities. In order to build this profile, potential clients must provide some sample data: a certain number of sample won deals or customers, examples of failed deals, or the definition of a target market (demographics, firmographics, buying patterns, etc.,). This data is usually extracted or synchronized with the CRM or the Marketing Automation platform in use by the potential client.

Derived from a core scientific principle of data analysis, the customer’s historical data is used to predict future activity. Models for lead scoring, opportunity scoringor pricing optimization rely on different datasets.

Since many vendors in the market use similar data science algorithms,differentiation is often based on data inputs and how they are incorporated

into predictive models, and not on the algorithm itself.

When a solution is used for predictive lead generation, the lead scoring model isbuilt by first analyzing the customer’s past client successes and theirbusiness environment. Then, thousands of sources of external behavioral data not found in the user’s CRM (from the web, social media, and other third-party data sources) are leveraged to

create the ideal customer profile (ICP).

The timeframe for the initial implementation varies from vendor to vendor. Set-upcan be completed in as little as one day, or can take up to two weeks. The amount of data to be analyzed and the number of models to be used are the two factors that most affect the implementation times for a given solution.

Building the ICP model

Data sources used by vendors

Datasets used for modeling can include:

Demographics: This data can include job title, department, hierarchical position, location, past companies, time spent in the current/past positions, age, gender, social metrics... Some vendors provide the ability to reach the lead (valid email, direct number, past responses...) into the scoring model.

Firmographics: Static company data can include the size, industry, location, financial metrics (revenue, margin, growth, etc), website metrics, activity description, existing customers, technologies they use, service providers they have.

Activity data in the CRM: Predictive vendors can leverage the activity data (at a lead or account level) existing in the CRM or marketing automation platform. This goes from past calls, status change, downloading an ebook, attending a webinar or visiting the pricing page.

External buying signals: External data is acquired at the corporate (mainly) or the lead levels. These are thousands of data sources that providers can leverage to build the DNA of ahigh-conversion lead. Digging into the types of sources each provider can work out is key. External dynamic data can be for example:

• The lead works in a department hiring +30% employees in the last 6 months• The company announces an internal expansion to a country you’re in• The company issues a job listing on the function X• The lead is a reference on one of your competitors’ corporate websites• A press article discloses that the company just raise VC funds• The lead is just appointed to a new position

Once a lead scoring model has been built for a customer, each new success or failure has to influence and impact the model. Market conditions change, prices changes, and the way sales reps work changes also. Vendors must give visibility on model evolution, either through their customer success teams or using a continuous evolution process with machine learning algorithms.

Building the ICP model

Delivering value: demo, process, routine

Most predictive analytics vendors will stress the importance of both establishing a clear goal for the predictive solution and arranging a demonstration of the technology as part of the evaluation process. The goal can be something along the lines of, “show me new leads for this offer/target, or, “score this sample of my leads/opportunities.” Even if the vendor’s demo runs on a limited set of sample data, a prospect is nevertheless able to see first-hand the value of the product, an essential prerequisite to purchase.

Vendors will also insist on the truism that no technology creates success all by itself. Instead, like any business solution, it is part of an equation which includes the people and the process implemented alongside the technology in place.

success = people + process + technology

In order to test if a certain vendor’s solution is compatible with a given business model, it’s essential to review case studies in which the technology has been implemented in a similar setting. Important to understand is:

• who in the team will use the technology and for what purposes? (such as BDRs for engaging new leads, sales managers for forecasting bookings, field sales reps for prioritizing opportunity efforts, marketing program managers for monitoring campaigns, customer success teams for preventing churn, etc…)

• what is the process for each user, what is the daily routine? (such as “engage 30 new leads a day” or “weekly review of an opportunity report in the CRM.”)

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At the core of the marketing technology stack

While there are 2,000 marketing technology vendors on the market fulfilling many marketing needs, from managing social channels to organizing content creation or running webinars, there are a few tools that are essential to most ‘technology stacks’:

• CRM: CRMs centralize all customer data (from incoming leads to opportunities, along with sales activities)• Web Content Platforms: Web content platforms such as WordPress host websites and most marketing activities• Marketing Automation: Marketing Automation platforms automate important marketing and sales activities like emailing campaigns• Predictive Analytic Solutions: Predictive solutions prioritize all marketing and sales activities, from acquiring new leads to converting new customers or retaining existing ones. They also allow for the accurate forecasting of future revenue.

“Scoring predictive scoring vendors”

• Vendors’ differentiators• List of existing customers• Starting prices and pricing models• Customer concerns

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