We’re excited to announce the latest release of the KnowledgeSEEKER, KnowledgeSTUDIO and StrategyBUILDER applications. Version 7.0 of the popular analytics suite delivers wider enterprise support and enhances its core analytics capabilities for credit risk and marketing while focusing on enriching the experience of its users.

We’ve listened…

In this release, we’ve added many new features as a result of customer feedback.

• Full data weighting support for profiling, segmentation and modeling.


• Strategy optimization capability to automatically assign treatments for strategy trees.


• Decision tree and strategy tree printing and print preview.


• New dataset and model analysis statistics: Spearman's rank correlation coefficients and the Gini index.


• Support for in-database analytics, a feature that allows calculations to be performed in the data warehouse, avoiding data movement to and from the software that slows response time.


• Wider server environment support with the addition of Red Hat Linux and Windows Server 2008.


• Save as PDF for all views and reports.


• Many more usability features.

More information about Angoss’ suite of analytics software

In general terms, CRM (customer relationship management) analytics is the analysis and presentation of customer data so that better and quicker business decisions can be made.

CRM analytics can provide:

• Sales team insight - which sales reps are closing more deals and how their cycles are


• Marketing campaign insight - track multi-channel campaigns from lead generation to closed sale and focus on most profitable marketing activities


• Predictive modeling - describe the likelihood that a customer will take a particular action


• Customer segmentation groupings - dividing customers into those most and least likely to repurchase a product; or dividing customers into industry verticals for effective sales or marketing initiatives or reporting


• Profitability analysis - which customers lead to the most profit


• Personalization - the ability to market to individual customers (or segments) based on the data collected about them


• Event monitoring - when a customer reaches a certain value/volume of purchases an action can be triggered, eg. offer training


• Up-sell/cross-sell insight - how likely is a customer or customer category that bought one product to buy more or a similar one

The benefits of CRM analytics lead not only to better and more productive customer relations in terms of sales and service but also to lower costs (as mentioned in a previous post).

As the year continues and there's no end in sight for the credit crisis, more companies are looking to do business smarter. They are looking towards cutting costs while their sales go down to maintain a profit. One method is to use software to analyze your historical customer data to predict future outcomes. Here are 5 ways to cut your costs by segmenting your data.

Segment the prospects who will buy from you and leave them out.
Don't contact those who would likely purchase regardless of an offer. Having identified this segment, you can save money by removing them from your sales or marketing campaign.

Don't offer discounts on customers who will stay with you.
You can't afford to offer retention incentives to all of your customers, especially the ones who will renew with you anyway. If you predict those most likely to leave, you can target much more efficiently.

Don't waste your time on prospects who won't respond.
By modeling on your data, you can see who is likely to respond to an offer, and then target accordingly. You will be targeting fewer people without sacrificing a high response rate.

Don't bother the customers who will stay.
You don't want to remind them that they're paying you, or annoy them with unnecessary offers.

Don't contact prospects or customers who wont pay.
You don't want to sell to prospects or customers who don't have the intention to pay. There's nothing worse than having the deal fall through because of a non-paying customer.

If the past several quarters have demonstrated nothing else, they have shown that financial services organizations are rethinking their reliance on traditional analytics methods and tools to support the needs of their business partners, as they grapple with new opportunities.

Looking forward, financial services organizations face two challenges:

First, analytics teams need to deliver better insight at a lower cost while boosting their productivity and reducing IT development, maintenance and support costs moving from analytics to action in their operational environments.

Use analytics software to address these challenges with confidence:

• Delivering productivity gains - perform data-driven segmentation and to create scorecards and strategies that can be developed and operationalized faster.


• Providing real value - Significantly reduce total software licensing and renewal costs while providing better analytics tools analysts will use more often.

Second, and on the bright side, the current economic environment presents numerous opportunities for analytics teams - how best to take advantage of these?

Now is the ideal time to target low-risk borrowers seeking to refinance legitimate mortgages, market for greater deposit growth, grow customer assets under management, improve your collection recoveries or enable managers to optimize the timing and value of foreclosed property sales.

Here's to thinking on the bright side!

The typical question relating to marketing analytics is: How does predictive analytics increase returns? The typical answer is: By directing decisions on which customers to target first.

However, in 2009, the landscape has changed and we turn away from increasing returns and focusing on deceasing costs.

    • Marketing more optimally means you can market less
                Segment prospects and effectively market to each
    • Sifting through prospects and finding high-conversion ones means you will spend less
                Uncover sales patterns in historical data
    • Retaining customers means less marketing to existing customers
                Gain insight into customer purchasing trends and maintain optimal contact with customers

Utilizing predictive analytics can fulfill the about three points and decrease marketing costs.

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.