Overcoming the CRM Data Deluge

Transcription

1 WHITEPAPER Overcoming the CRM Data Deluge Sales Effectiveness with Best Practice Customer Analytics

2 You are reaping the rewards of a successful CRM implementation. Your customer-facing business processes are more efficient and all the interaction with your client and prospect bases across your organization is better orchestrated and recorded. Tracking reports are identifying activity and status on your leads and opportunities, and while forecasting isn t yet as reliable as you need it to be, the process is instilling a healthy discipline in your sales force that will pay dividends as it s refined. Despite these successes, the cost of sales, (typically your net sales expense) is still too high and hasn t materially improved as a result of your CRM implementation. While processes are now streamlined, this hasn t translated into gains in sales force productivity. Sales support, marketing and operational costs continue to rise and yet individual quota carriers are not increasing their close rates accordingly. The process of marketing to prospects, identifying and qualifying leads and converting these into opportunities remains inefficient. Marketing campaigns are expensive and return a low single-digit response rate. Leads need to be qualified and converted into legitimate account opportunities, and only small fractions ever do so. Fractions of fractions are involved small numbers and the options for mining the leads that matter are few. Third party screening agencies come at a cost and often are a drag on internal resources as they are trained to understand the sales message and qualification criteria. An inside sales team is frequently more effective at lead conversion but in the process is burdened by an unproductive search for the fractions of fractions those elusive leads that matter. In many cases, companies find themselves with these challenges high net sales expense with few options to eke out productivity improvements as a result of a CRM data deluge. Symptoms of the CRM Data Deluge While it s an unfair accusation to suggest that CRM solutions themselves exasperate data deluge problems, these types of challenges are surprisingly common in organizations that sell into small and mid-size businesses, where there is a pressure on funnel maintenance as sales cycles are short and individual deal sizes are smaller: 1. Leads and opportunities that vastly outnumber possible sales coverage While Marketing has been tasked with generating leads to keep the funnel in good shape, they may have done this too well. Possibly, sales and marketing aren t in perfect alignment and marketing is conducting programs and generating leads independently of what sales can manage. As a result, there are an overwhelming number of leads in the CRM system. 2. Data hygiene issues: Sales hits productivity speed bumps every time valuable fields of data are missing. Sketchy contact details, the product or service of interest, and lead source (to name a few) will slow down the conversion process. Duplicate contacts or leads split record history and contribute to an inexact view of the account base. 3. Ineffective lead and opportunity scoring: without an effective scoring system for leads and opportunities your sales team is required to poll the entire database of leads in order to find the convertible opportunities that are embedded randomly in its folds. Think of the needle and haystack without a metal detector. No one knows whom to interact with, and when. 2

3 Overcoming Data Deluge Challenges To a certain extent, managing the demand generation process is like optimizing an internal supply chain. As a supplier, Marketing is needed to deliver exactly the right type and number of leads (no more or no less) at the right time. Having delivered to order, Sales then assumes the responsibility for lead inventories such that the pace of conversion is consistent with changes in volume. Tactical data hygiene issues are resolved with policies that can be enforced in the application. As a supplier, Marketing is responsible to lower its lead defect rate. While firming up sales and marketing alignment would help address these issues and ultimately make funnel management more efficient, many organizations in this situation are a long way from implementing these types of organizational changes. The compelling problem remains one of vast amounts of lead data without any productive means to filter out the real opportunities. With leads in particular, time is a factor. Good leads go stale very quickly, so without prompt action their likelihood to convert plummets, impacting not just sales productivity but also any return on the marketing investment made to originally generate the interest. As daunting as the data deluge may seem, such environments are ironically some of the richest for predictive analytics solutions. Unlike most business intelligence that delivers rear view mirror insight into historical trends, predictive analytics mines historical data to detect patterns in transactions that can then be used to predict future customer and prospect interaction behaviors. Predictive analytics can be very well-applied to the data deluge problem in fact the bigger the better as the filtering it can provide will deliver proportionally greater levels of efficiency. While lead scoring has become a popular term covering a wide range of business rules and attribute driven applications, none of these can really challenge the authority predictive analytics brings to the term. Far from insight-for-insight s sake, it answers the question, Of the overwhelming number of sales leads in my CRM system, which ones will most likely convert? Unlike simple red, yellow, green type systems force rank all leads... from top to bottom based on their conversion probability. Focusing on the sample of leads most likely to convert dramatically reduces the effort to find and develop the ones that matter. Unlike simple red, yellow, green type systems, with predictive analytics you can force rank all leads in a system from top to bottom based on their conversion probability, significantly improving the way in which sales teams operationalize their funnel management. 3

4 Understanding Return There is a real return that can be calculated from the lift provided by predictive lead scoring. One of the most practical aspects of predictive analytics is that the models that are developed also describe their potential ROI before they are ever deployed, allowing businesses to assess their likely benefit before investing in their implementation. The following graph depicts a realistic lift curve that a predictive lead scoring model would generate. This lift curve illustrates that the predictive model provides for 75% of the lead conversions to be generated from 20% of the lead population (i.e. leads in the top two deciles as indicated by the model). As with all analytics models, achieving predicted return depends on a number of human and operational factors. Also, as sales cuts through the data deluge challenge and converts opportunities more effectively, the onus is back on marketing and sales support to maintain a funnel of prospects that can continue to act as fuel for the analytics engine. 4

5 Let s examine this through a practical example: A business process outsourcer sells its services into mid-market organizations. Our BPO vendor maintains a team of four inside sales associates. Their average deal size is 5,000 and they can make 20 calls per day to aim for annual quotas of 220K each (52K per quarter). In their current state of effectiveness and lead data quality they convert 4% of their leads into opportunities and twenty percent of active opportunities close each quarter. Without even changing the number of calls or the quality of data, the benefit of scoring leads is material. While making the same 20 calls per day, each inside sales associate could increase their lead conversion rate from 4% to 14%, ultimately generating an additional 500K in additional sales per rep, per year as outlined below: Current Performance Predictive Lead Scoring Return Lead Conversion 4% 14% Opportunity Close Rate 20% 20% Lead-to-deal Close Rate 0.8% 2.8% Average Deal Size 5,000 5,000 Prospect Calls/day Deal Closes/day Deal Closes/quarter Quota Attainment/Quarter 52, ,000 5

6 About Angoss Software As a global leader in predictive analytics, Angoss helps businesses increase sales and profitability, and reduce risk. Angoss helps businesses discover valuable insight and intelligence from their data while providing clear and detailed recommendations on the best and most profitable opportunities to pursue to improve sales, marketing and risk performance. Our suite of desktop, client-server and indatabase software products and Software-as-a-Service solutions make predictive analytics accessible and easy to use for technical and business users. Many of the world's leading organizations use Angoss software products and solutions to grow revenue, increase sales productivity and improve marketing effectiveness while reducing risk and cost. Corporate Headquarters 111 George Street, Suite 200 Toronto, Ontario M5A 2N4 Canada Tel: Fax: European Headquarters Surrey Technology Centre 40 Occam Road The Surrey Research Park Guildford, Surrey GU2 7YG Tel: +44 (0) Copyright Angoss Software Corporation 6