Guide to Predictive Lead Scoring

Transcription

1 Guide to Predictive Lead Scoring

2 Lead scoring has an important role to play in modern B2B marketing and sales. It is a useful system for gauging a prospect s likelihood of buying, allowing salespeople to prioritize their targets. In this guide, we will cover what lead scoring is in more detail, the ways it can be improved through predictive analytics, and how and why you should use predictive lead scoring system to stay ahead of your competition.

3 What is Lead Scoring? Lead scoring is a methodology to rank prospects against a scale that represents the perceived value each lead represents to the organization. The resulting score is used to determine which leads a receiving function (e.g sales, partners, teleprospecting) will engage, in order of priority. I Through assigning scores to prospective customers or clients to gauge buying intent and readiness, the following benefits can be achieved: Increased sales efficiency due to the prioritization of leads. Marketing efforts made more effective. Tighter marketing and sales alignment. In a typical lead scoring system, a prospective customer will have a variety of criteria that makes up their score. With attributes like company, budget, and location, the prospect is assigned a score of how likely they are to buy. If we add in actions such as signing up for a newsletter, clicking on a sales link or filling out a form, we add behavioral criteria that adds to the score. All Names Prospect Engaged MQL Sales Lead Opportunity Behaviors Early stage content: Attend webinar: Visit any webpage/blog: Visit careers pages: Pricing pages: Watch demos: Mid-stage content: Late-stage content: Demographics Job Title: Generic Industry: Technology: Customer

4 Problems with Traditional Lead Scoring Lead scoring tools require users to select the attributes and actions on which to score the leads and to determine the actual score. However, it s largely down to human intuition to set the values and weights in the first place. Most marketing automation providers recommend sales and marketing teams have a large brainstorming session to decide what is an ideal lead and its score. This appears good on the surface and it asks for cross-departmental cooperation. However, the resulting lead scoring is based a lot on assumptions the team makes. They may be educated assumptions, but assumptions none the less. This lead scoring system - one that relies on assumptions, can quickly result in sales disenchantment, to the point where good leads are routinely ignored and demand generation ROI suffers accordingly. Companies often launch scoring schemes that just don t work; by the time the scheme is modified, sales teams need to be re-sold and re-trained on what they now regard as inaccurate and irrelevant data. Even if sales teams haven t been through this process, they may opt to ignore the scores attributed to leads and use their intuition to pick the winners. It can even happen that the marketing team effectively guesses the lead scoring scheme and the sales team ignores the scores entirely. This is bad for the marketing department, as lead scoring optimization is a critical part of ensuring that companies reap the greatest benefit from the investment in marketing technology. It is doubly bad for sales, as they spend a lot of their time chasing leads that don t convert and most likely pass over leads that are great fits for the company.

5 Big Data and Predictive Analytics A SiriusDecisions report stated that 94% of all Marketing Qualified Leads will never convert II. This means with all of Marketing s efforts only 6% are leading to customers. Evidently there s some way to go in improving the lead scoring processes. This is where predictive lead scoring comes in. The first step in building a lead scoring process that works is to accurately define what success looks like for the organization. In the cases of most B2B organizations, success looks like closed won opportunity information inside of the Customer Relationship Management ( CRM ) system. Once success is established, the next place to look is at the digital footprint of that company. Your customers all have a digital footprint online through their social networks like Twitter, blogs, press releases, job openings and the technology on their corporate website. This abundance of data on the public web and in your CRM system can be leveraged to find the ideal customer for your business. But rarely is this data well structured; any organization with a CRM has been collecting a goldmine of data, but most are not leveraging it to draw actionable insights and find meaningful correlations. Predictive analytics can help. Through machine learning a predictive model can crunch through large data sets and structure data properly to deduce patterns that went previously unnoticed. Predictive lead scoring companies like Fliptop are able to take this data structure it properly. We re also able to find previously unseen relationships between data sets and predict likely outcomes through predictive modelling. Predictive modelling is a commonly used statistical technique to predict future behavior. Predictive modelling solutions are a form of data-mining technology that work by analyzing historical and current data and generating a model to help predict future outcomes. In predictive modelling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. III

6 In the near future, predictive engines tied into marketing automation systems will be able to automate the entire process of assigning scores to activities, so marketing teams no longer have to guess the value of downloading a white paper or attending a webinar. How Predictive Lead Scoring Works A predictive model always starts by understanding what success looks like and for most B2B enterprises, this is closed won opportunities. The system can analyse thousands of public and internal data points in order to construct a fingerprint of what a good lead or account looks like. This information could include fields like: Size of company, revenue, legal status Social network memberships and activity Marketing actions - e.g. prospect clicked A, B or C Technology the company uses on their website It starts with your existing CRM opportunity data It adds thousands of person and company attributes We automatically create a predictive scoring models tailored for you It applies scores to your lists or directly within your sales and marketing applications

7 Once the predictive algorithm has learnt which data signals typically result in a sale, new leads can be scored instantly. No longer are marketing teams throwing all the leads over the fence to sales and waiting weeks or months to see the results. Because most marketers are not doing any form of predictive analytics today, the potential for upside is huge. Imagine if your business could definitively answer the following questions: Which leads should marketing send to sales, nurture or discard? What accounts or contacts should sales be prospecting with next? Which programs should marketing scale back or double-down on? How likely am is sales to make quota this quarter? Which accounts are ripe for cross-sell/upsell? Which accounts are most likely to churn? In the absence of predictive analytics, these questions inevitably are answered by HiPPO - the highestpaid person s opinion. Those who operate by gut instinct are at a distinct disadvantage in the marketplace against those who leverage data.

8 5 Reasons You Should Adopt a Predictive Lead Scoring Technology Predictive analytics clearly gives a multitude of key advantages over traditional lead scoring models. In summary, five of the most important factors for choosing a predictive lead scoring technology are below: 1. Base decisions on data, not intuition many established lead scoring systems are set up based on intuition a predictive technology removes this risk. 2. See the bigger picture through more data points since predictive lead scoring technology can use thousands of different signals and data points, you re likely to get a much deeper understanding of your prospects. 3. Understand previously unseen patterns through algorithms these signals are sent through a machine learning system, which can spot previously unnoticeable patterns in prospect behavior. 4. Machine learning will optimize your lead scoring patterns over time, your lead scoring system will have a deep understanding on the behaviours and combinations that drive a sale specific to your market, removing possible errors of human judgement. 5. Get a closer alignment between marketing and sales an improved lead scoring system will almost certainly lead to a better alignment between these two departments, given marketing qualified leads will have a better chance of converting. How You Can Get Started with Predictive Lead Scoring? If you would like your own predictive scoring model, please contact us at [email protected] or call

9 References I Famico, Jay, What is Lead Scoring, Anyway?, January 14, II Sirius Decisions III Gartner IR Glossary