Analytics Brought to Bear
Salespeople crave customer information. They hunger to gain that critical piece of insight about a customer that spells the difference between an opportunity and a closed deal. And for as long as there have been SFA applications and CRM systems collecting and storing customer data, salespeople have sought the means to compare and contrast that data. Enter business intelligence (BI) and analytics tools.
The use of BI and analytics by an organization's sales force isn't new--vendors and suite providers had been embedding analytics and reporting functionality into sales applications long before Marc Benioff ever founded Salesforce.com. What are new are the solutions that BI vendors now offer and the way sales forces are leveraging these tools' capabilities to analyze and even predict a customer's purchasing habits.
There are three levels of maturity, or tiers, that a company might inhabit when it comes to its sales team's use of BI and analytics (see the sidebar, "Level Best"). The first maturity level is a basic-metrics comparison state. The second level uses keener reporting abilities that are more customized. The third level accesses advanced information that can include across-the-enterprise data.
Then and Now
Five years ago companies consistently operated at levels one or two, as tier three represented not only the future for SFA and analytics apps, but also the importance of linking enterprise apps via data warehouses and data integration software. But as companies ascend this maturity ladder, they're leaving behind basic BI for heavy-hitting analytics, says John Hagerty, vice president of research at AMR Research. "There is a difference between BI and analytics. BI is more about a source of data that you want to slice and dice, and then report to a group of people. Analytics is when you want to analyze data in the text of a specific issue, such as understanding segmentation trends or propensity to buy. You're into analytics applications." The two may have similar attributes, but it's important that companies understand the differences. "BI," says Kathy Konkel, product marketing manager at SPSS, "has traditionally been more for the sales force and analytics more for the marketing department."
Today, it's more about the pull than the push. In years past analytics reports were given, or pushed onto a sales force from management. Whether the data was of any direct value depended entirely on a salesperson's ability to dissect that information in an Excel spreadsheet. But with the increased emphasis by SFA and BI vendors alike on user friendliness and reporting, salespeople are using these capabilities to pull the data themselves, and more often. Salespeople used to receive reports from management on a monthly or even quarterly basis. Now they're pulling data themselves weekly or even daily.
This ability allows salespeople to focus less on traditional sales data and more on customer information. Sales forces now provide themselves with precise answers to questions relative to the profitability of individual products and customers, and to the success of sales promotions. They can analyze customer buying patterns and trends as they develop, and they can see into the overall dynamics of the sales equation by having the ability to analyze bookings, lost sales, and inventory stock levels. Analysis by company, customer, salesperson, territory, item, warehouse, and item division or any combination of these criteria can now display profitability, yearly comparisons, trend charts, and rankings. "It's becoming less about [generalities]," says Jill Dyche, cofounder and principal at Baseline Consulting. "Teams [are]taking sales data and slicing it to reveal customer trends and purchasing triggers."
Two such triggers are pricing and product availability, and BI and analytics are providing salespeople with the numbers to eliminate some of the guesswork. Salespeople are increasingly leveraging product, pricing, and inventory data from supply chain management solutions to gain a better understanding of which products can be offered at what prices. "You can determine what types of products customers tend to purchase together, what sort of products and services are price sensitive and what aren't," Hagerty says.
Monterey Mushrooms understands the importance of all this. The company uses analytics to not only drive its sales force, but to gain a competitive advantage over regional competition. As the largest--and the only nationwide--producer of fresh mushrooms, the company is in a unique position, according to Mike Matelli, director of information services at Monterey. Because anybody can grow mushrooms, the problem becomes how to add value to its product offerings. "We needed to differentiate ourselves to compete in a market that is basically regional." The company turned to SPSS for help.
The mushroom company used to provide its sales force with traditional, green bar reports, according to Matelli. Salespeople would thumb through them, extract any meaningful or relevant data, and then be forced to reenter that information into Excel spreadsheets before they could analyze the numbers. Now, Monterey uses SPSS to create OLAP reports. But this time saver is just the beginning.
Monterey started with the basics, supplying its salespeople with raw sales numbers like quotas, territory data, and pipeline information to do compare-and-contrast analysis. Last year the company added costing information, that is, shipping or packaging costs, allowing salespeople to analyze and integrate profit margins into their sales pitches. "The customers control the market," Matelli says. "We sell products at a delivered price, so being able to analyze the margins and focus on specific costs before talking with our customers is huge." The company also uses an analyzer to forecast sales figures for 2007, a capability that wasn't available with the previous setup. Prior to that salespeople generated summary numbers for particular sales regions. Today, forecast figures are calculated for specific stores, and thanks to the addition of costing capabilities, Monterey's operation department uses SPSS to improve performance and minimize cost overruns.
Looking forward, Matelli says financial data from accounting will be added so the F&A department can leverage the application, giving the sales force another category of data to cross-reference. Eventually predictive analytics will be used to predict the orders a customer's distribution center will request by adding point-of-sale data from the individual stores that distribution center supports. "[The food stores] are trying to cut their costs, so they're pushing the pressure on the vendors to do that," Matelli says. "SPSS will allow us to do that without increasing our costs."
The Future Is All Together
The future of sales analytics lies in the convergence of data. As companies break down silos, the types of information available to cross-reference data will increase, making predictive analytics possible for the first time. Above all, analytics will bring the sales and marketing departments together. "Companies will combine all the information they have about customers, their orders, the product bundles, the prices, the segmentation, and the marketing programs so you understand what certain groups of your customers look like," Hagerty says. "It's great to know that people are responding to certain marketing campaigns, but if you don't do anything with that information, why ever look at it?"
Salespeople may never directly use heavy-hitting analytics, but they will do a better job of leveraging data others have created via BI solutions. The marriage of sales and marketing will only facilitate this. "I don't see salespeople using SPSS or SAS directly," Dyche says. "Marketing will provide sales with mining intelligence that they've been keeping to themselves, allowing salespeople to bundle certain products and services together."
Vendors are responding to this emerging trend. Many SFA and CRM suite providers are partnering with best-of-breed BI and analytic vendors, such as Salesforce.com with Business Objects via AppExchange. Other niche players are solving this problem by operating in a space called prospect development management (PDM). Before the Call, an on-demand provider of PDM, offers a product entitled myMarketSpace, which applies market analytics to show salespeople what opportunities are being pursued in their accounts, what are available to pursue from the Market Space database, and what opportunities exist that aren't yet in their SFA application, such as from marketing. "These types of data solutions provide an integrated way to understand and attack a market by bringing the prospect intelligence found in marketing and the execution of sales into one application," says Barry Trailer, an analyst with CSO Insights.
BI tools are making their way onto BlackBerrys, Treos, and Smartphones just like their CRM and SFA brethren before them. These solutions provide road warriors with basic sales reporting and forecasting, and access to reports via wireless synchronization with the office. "It's all about deliverability," says Mark Morton, senior product marketing manager at Cognos. "It's as much about the delivery vehicle as it is the application because salespeople want the information in a timely way."
Perhaps the best example of a vertical that's pushing the sales analytics envelope is the pharmaceutical industry, long known for its large sales forces, complex sales plans, and multifarious product portfolios. Recent environmental pressures have forced this industry to adapt. In the past, pharma companies used a marketing tactic called RFM (recency, frequency, monetary) to bombard doctors via enormous sales forces. The results were bloated sales forces and fat reps whose overall monetary value to the company was less than their cost. After years of double-digit growth, government regulations on drug pricing and stiff competition from foreign manufacturers put a stranglehold on the growth of these sales forces. In 2005 Wyeth announced plans to cut its sales force by up to 30 percent by eliminating or shifting to part-time some 750 full-time sales personnel. "The industry reached a point where the growth of its sales forces was no longer tenable," says Zach Henderson, director of sales force effectiveness for the Americas, IMS Health, a consultancy that provides market intelligence to the pharmaceutical and healthcare industries.
Faced with the prospect of doing more with less and possessed of a crystalline vision of profitability, many pharmaceutical companies are turning to companies like IMS Health and to analytics to streamline sales operations. IMS supplies companies with market data and analytics using SAS's Enterprise BI Server and related applications to power the IMS Precision Sales Force Offerings. IMS allows clients to understand what drives the behavior of physicians via segmentation and predictive analytics by measuring doctors' responses to different types of promotions. IMS measures prescription data at the physician level (which drugs a specific doctor or type of doctor is prescribing the most), at the prescription level (Aetna, U.S. Healthcare, et cetera), and at the longitudinal prescription level (across certain medical and treatment plans), and cross-references that against known industry trends. "We'll survey 400 doctors about their attitudes towards drugs and we'll use analytics and predictive modeling to lay those 400 attitudes onto 20,000 doctors," Henderson says. "We can usually achieve a 95 percent accuracy rate." Pharma companies leverage this information to refine sales pitches and decide if a med-sci liaison might make a better representative.
Certain clients are taking analytics a step farther by adding resource optimization to the mix, allowing companies to structure their sales force around a product that is still in development. Wyeth cut its sales force based primarily on a three- to five-year sales forecast of what was coming down the pipeline. Based on predictive models that examined doctor responses to new product introductions, the company realized it didn't need the same level of promotion to sustain sales, or had the product portfolio to maintain its swelling sales force. "This is very different from the rear-view-mirror approach that has been used," Henderson says.
So, back to the beginning: Salespeople crave customer data, but need it to be timely and convenient. The key to combining sales and analytics, and sales with marketing, will be solutions that deliver this increasingly complex and predictive data in an easy-to-understand and opportune format. After all, Dyche says, "Salespeople just really want to sell."
Contact Assistant Editor Colin Beasty at cbeasty@destinationCRM.com.
The Wow of Web Analytics
Time spent on a prospect immune to a sales pitch is time wasted. Even the most experienced salespeople fail to close deals when faced with a customer who is just looking for information. This is particularly true when it comes to Web analytics. "Lead qualification to sift the tire-kickers from those really interested in buying is not as difficult as it used to be," says Martin Schneider, senior enterprise software analyst with The 451 Group. "Web-analytics tools and other tracking software are enabling companies to get a handle on who's just browsing versus who has been doing real prepurchase research."
Faster access to better info lets sellers reach the entire spectrum of buyers and establish treatment paths for each. A company might require salespeople to contact a high-priority lead within an hour of an email or phone message, or might set up an email schedule to reconnect with shoppers doing early research. "If Web analytics is tied to the lead tracking system in a company, sales agents can see where an individual has been online prior to or after responding to a promotion," Schneider says. "Those who spent time on three or more related sites might get scored as 'hot' leads, versus those who simply clicked on the URL in an email and made no other visits. Leads can be processed accordingly, and agents can spend less time selling to colder leads." --C.B.
A Company's Level Best
3 BI/Analytics Maturity Tiers
Level 1: A sales force uses the canned reporting features native in any SFA system, whether it's a best-of-breed or suite solution. Sales analytics are being used to contrast basic metrics, such as comparing a territory, sales team, or individual salesperson against quotas or other sales teams and/or territories.
Level 2: Analytics and reporting capabilities are more customized, and the metrics against which management measures are more precise. Sales managers are now measuring against quotas from a previous year or against forecast reports for the upcoming year, and against bonuses and commissions. "It's more complex and requires more out-of-the-box customization on the SFA side," says Jill Dyche, cofounder and principal at Baseline Consulting. "Any analysis will also represent the vertical the company operates in."
Level 3: Dyche calls this tier analytics for the advanced placement sales force. Management can analyze and produce sales reports that measure data against other forms of information that are not directly analogous to the sales force via best-of-breed BI solutions. Salespeople are provided with reports that include product, marketing, and financial data to gain "a true 360-degree view of the customer." The key to tier three lies in a company's ability to move the sales data from a single repository into a data warehouse that combines all forms of data from various enterprise applications, says John Hagerty, vice president of research at AMR Research. "Most companies store their sales data in a single data repository that is external to other enterprise applications. Once you begin to bring that information together into a central core, that's when you move away from the analytic and reporting functionality found in an SFA system and you start leveraging best-of-breed solutions [from vendors like] Cognos, Business Objects, or SAS." --C.B.