Behind all the news headlines about credit losses, write downs and foreclosure there are collections departments working overtime. Not only is recovery in current times difficult, but it’s also expensive. Collections are the highest operational cost once an account is on the books with 80% of the operational cost of a portfolio attributable to collections costs and write offs.

While a small improvement in recovery results can make a significant impact on operations, collections strategy challenges abound – who, when and how should businesses focus their collections efforts in order to maximize recovery, minimize costs, and maintain (or improve) customer satisfaction?

In traditional collections operation, activities were based on outstanding balance, days past due, and credit score. Early stages of delinquency brought less activity with less experienced agents, while in later stages, more experienced agents took on more aggressive tactics. Not only does this model not scale well to address situations like we see in the current climate, it’s also inefficient. Many of the early stage accounts would likely have paid without any intervention whatsoever.

This is where applying predictive analytics to segment debtors becomes critical to triage the AR portfolio. Those we can segment and predict as ‘sloppy payers’ – customers that simply pay late - can be ignored as they typically pay without any action taken at all. Those we segment as under economic hardship are likely to pay if sufficient action is taken. And fraudsters, they won’t pay regardless.

Once we have segmented our portfolio, we can define and assign strategies and treatments. In early stages of delinquencies, for example, there are large numbers of ‘sloppy payers’ and here we could apply a strategy where we target those least likely to pay (i.e. those under economic hardship). Sloppy payers will pay anyway (so it’s a waste of resources to focus on them), and in fact may suffer a hit in client satisfaction if annoyed by collection efforts. In later stages, debtor segmentation would help us target those most likely to pay – collections are a competitive environment and late-stage debtors likely have other creditors.

While all of this sounds simple, there are some key areas of analysis required in order to get good predictive results in our debtor segmentation. We’ll pick this up in our next post.

Your customers and prospects are predictable. They engage, inquire, negotiate, purchase, and yes, sometimes even attrite, according to quiet little patterns etched into transactional and behavioral histories stored in your very own CRM system. Organizations focus predominantly on using this data to foster insight into understanding “what happened” or “what is happening”. And while applying a BI strategy to grasp ‘rear-view’ sales and revenue trends is important, the same pool of customer data remains a largely untapped source for valuable predictive insight.

There are two basic business challenges in the sales channel that operational CRM systems cannot fully address, as effective as they are at managing the orchestrations around our client contacts. First, there is the cost of sales itself, or your net sales expense. Sales representatives need to build pipelines, develop accounts, and mine leads in order to ultimately close business. These activities, as well as the efforts of those that support this process, are all costly. Sales and business leadership remains under increasing pressure to meet targets without increasing cost, which can only be accomplished through further improvements in productivity.

Secondly, sales teams - and the sales representatives and account managers that comprise them - are given to very human foibles, and not least among these is the emotional subtext that enters into the sales process. The purchasing intentions of clients and prospects are mediated and interpreted by sales who in turn apply the sum of their experience and intuition to forecast outcomes of these engagements. As a result, the aggregate forecast for which the sales executive is ultimately accountable is only as reliable as the individual contributions of those very human sales reps and account managers themselves.

Both these issues are fundamental – how can organizations become dispassionate in their interpretation of buyer intentions, and how further can sales productivity improve?

Given that wealth of untapped sales and customer data, this is where predictive analytics comes in. Consider some of the inefficiencies in the demand generation process. It is common for organizations to maintain thousands or even tens of thousands of sales leads in their systems and yet statistically only a very small percentage of these ever convert. One relevant application of predictive analytics is the indication of which leads from a database of several thousand actually matter, such that sales teams are able to bolster productivity by converting more leads (and hopefully closing more business) with dramatically fewer calls. Similarly, consider the scenario of the sales manager looking at a pool of open opportunities and a looming quarter end. The most productive use of resources would be one where the sales leader could ensure coverage of the opportunities most likely to close by the end of the quarter, another application of predictive analytics.

Predictive analytics can also address the human issues inherent in forecast reliability. Individual interpretations of buying propensity give way to a scientific extrapolation of forecast results that not only applies data mining and statistics to improve accuracy, but also helps to eliminate the surprise factor that impacts sales teams at the end of fiscal periods often regardless of their maturity. Many organizations also use predictive forecasts (and its delta with an individual’s forecast) as a coaching tool to improve sales forecasting over time at the individual level and team level.

The breadth of applications for predictive analytics for sales and marketing are many. With the maturity of CRM systems and the wealth in client data they are silently accruing, the convergence of these technologies is not only complimentary, but imperative.

Everyone knows their best customers - those flagship accounts that place large orders, renew consistently, and lend us the use of their logo when we ask nicely. We know their buying behavior and price sensitivities because we speak to multiple contacts in their organization every month and twice a year we have lunch. Because these accounts are so valuable we have dedicated account managers that are responsible for the experience of these clients.

But what about the masses of customers that constitute the bread-and-butter of our revenues? What do we know about them? When do they buy and when do they renew? What makes them attrite? When and how can we reduce the risk of attrition and improve the likelihood of renewals and repeat orders, considering that we cannot afford to manage all these accounts with the same personal attention we give our flagship customers? How do they prefer to be serviced?

In short, how do we manage the customer experience for these clients in a way that optimizes our marketing, sales and service investments and drives the greatest levels of profitability? Any strategy we put in place absolutely needs to start with that timeless imperative – know your customer – and with the sophistication of BI tools today this means strategic segmentation.

To be effective, segmentation needs to be strategic in two ways that often differentiate it from its more tactical cousins. First, the way we segment our clients needs to consider a number of data points or behaviors. Applying simple rules to stratify based on a single variable, like revenue or product line, will not drive the results one would hope to achieve. Second, our client segmentation must resonate outside the Marketing department. Once having developed and harnessed strategic segmentation as a backbone of a customer experience strategy, all areas in the enterprise that have a role in the customer experience must be culturally socialized to think and interact with our clients in the context of the segments that define their buying, attrition and service behavior. Operationally we can integrate and propagate segmentation information in our existing CRM/CEM systems and ensure we align our marketing programs to acquire, develop, and retain our customers according to their segment profiles.

While we can’t take every client to lunch, we can often use the insights of strategic segmentation to know our customers just as well.

Marketing has just completed a demand generation campaign around a promising new vertical and as a sales leader, you find yourself the proud new owner of 2000 new leads and yes, your counterparts in Marketing will be following up in 60 days to see how many have converted. Mentally you do the math and with a 5% conversion rate that’s a possible 100 new opportunities.

This sounds good, except that in order to get the 5% (or 6%, or 7%) your sales team needs to contact all 2000 leads, and a 5% conversion rate means 1900 calls that are a necessary evil in the qualification process.

What if you could convert the same 100 leads, but with say, only 400 calls, imagining the same savings in sales productivity for each and every campaign through the year. We were recently speaking with a client that explained that their demand generation team was deliberately providing fewer and fewer leads to sales in order to minimize this burden - how many of us find ourselves in similarly backwards situations?

Predictive analytics isn’t new – it has been used for decades in credit and risk scoring and more recently in sales and marketing functions. Given the current economic climate, improving sales productivity through lead scoring is just one of the challenges that predictive analytics is addressing for businesses of all sizes. Rather than have your sales team ferret out those 100 opportunities from 2000 new leads, leverage predictive solutions to score all 2000 and provide your team with the results effort through a seamless integration with your CRM system, driving the same opportunities with a fraction of the sales effort.

And yes, your counterparts in Marketing will be pleased with the response, too.

If ever there was a well-made match in the universe of technology integration, the combination of CRM and predictive analytics has to fall near the top of the list. For the benefit of CRM stakeholders - marketing professionals, account managers and sales leaders - predictive analytics help fulfill the promise of CRM and sales force automation.

A bold statement perhaps, but consider the facts: according to April 2008 research by CIO Insights, 70% of companies surveyed already have CRM, with a sizable number (43%) of the remainder moving on it within the next 12 months. Beyond the kudos that these types of numbers earn the SFA/CRM industry, for companies deploying CRM it means that the operational efficiencies that your system has institutionalized (or not!) can no longer be considered a competitive advantage.

Once having successfully deployed your solution, unfortunately many of the same, very human, and potentially undermining challenges remain. Yes, all your account managers now use the new system to track opportunities and contacts and yet humans still don't predict closure rates dispassionately. Marketers are targeting campaign prospects based more on instinct than science. Sales leaders need to yet further reduce net sales expense while managing aggressive quotas in somewhat challenging economic times. And yes, forecast reliability remains an issue - the aggregate success or failure of so many account managers' and sales reps' interpretations of their pipelines.

Predictive analytics resolves these very human foibles for CRM stakeholders with algorithmic, testable lead and opportunity scoring and forecasting. It compliments, not competes with the collective effort of marketing and sales teams and the operational best practices CRM instills. It makes forecasting more (scientifically) reliable and provides the ‘lift’ enabling sales to focus strategically on opportunities with a greater predictive probability to close – especially useful if managing so large an account base that priorities need be established.

But the benefit of this pairing should be considered mutual. CRM brings predictive analytics two things that frequently undermine the latter’s own success - large amounts of well-formatted and reliable sales and customer data coupled with an instant (and dare I say elegant) deployment to the business users that need to consume the results. Integrated seamlessly with current CRM solutions, analytics results are no longer stuck in the ‘basement’ or back office.