You don’t have to search too hard to find breathless paragraphs on the power of analytics. And there are plenty of examples in the sales world where analytics is delivering significant improvements in growth, efficiency, and effectiveness.
In our survey of more than 1,000 sales organizations around the world, we found that 53 percent of those that are “high performing” rate themselves as effective users of analytics (Exhibit 1).
Yet for all the tangible benefits, analytics is still a bit of a sideshow when it comes to sales. The same survey shows that most sales organizations today (57 percent) do not view themselves as effective users of advanced analytics. Many companies struggle to benefit from even basic analytics, while some have yet to even dip their toes in the data lake at all.
Well-designed analytics programs deliver significant top-line and margin growth by guiding sales teams to better decisions. But that only happens when companies can do two things well: focus on areas where analytics can create the most value, and implement wisely.
Focus: Four of the biggest sources of value
Forward-thinking companies are using the growth of data analytics and artificial intelligence to expand the frontier of value creation for B2B sales and are generating remarkable results in lead generation, people management, cross-selling, and pricing (Exhibit 2).
1. Radically improve lead generation. Analytics is well-suited to improving the accuracy of lead generation and automating presales processes as companies use rich data sets to identify the right customer at the right time.
Many companies already use historical market information to develop a detailed view of each area’s sales prospects. Some companies are pushing this further by introducing lead-scoring algorithms based on detailed and granular data sets on each of their prospects. Internal data sources on the customer’s previous history are combined with rich external data such as news reports or social media to generate a “360 degree” view of the customer. These algorithms can then predict which factors truly matter in lead conversion and guide sales strategy accordingly. One IT services company used such big-data analytics to predict which leads were most likely to close—and found that established companies were better prospects than the start-ups it had been focusing on. Focusing its attention on established companies raised its overall lead-conversion rate by 30 percent.
As these greater predictive insights are combined with intelligent automation, companies are seeing a leap in their ability to identify opportunities and convert them. Several companies are experimenting with AI-enabled agents that use predictive analytics and natural-language processing to automate early lead-generation activities such as handling basic customer questions and automating initial presales questions.
Aside from being much more efficient than traditional approaches, these algorithms can identify the most promising prospects and pinpoint the most opportune time to target them.
2. Better match people to deals. Similar to the way analytics revolutionized baseball by revealing the true factors underlying wins, sales forces are using analytics to understand what drives sales success and to inform coverage, hiring, and training.
Traditional sales planning has relied on account segmentation, often determined more by historical local knowledge than up-to-date facts. The result is that, over time, sales models become less effective and globally inconsistent, while resources are poorly allocated to accounts that require different types of sales strategies (e.g., grow versus retain).
However, when sales-operations teams introduce basic analytics to sales planning, resource allocation quickly becomes far more effective. A high-tech company used a granular account and product-level approach to realign its US coverage model. Sales productivity rose 5 to 10 percent, and the sales staff cut its planning time by two-thirds.
Analytics is also revolutionizing our understanding of sales talent and field behavior. In the quest for the highest-performing salespeople, organizations are combining sales, customer, and HR data to understand the intrinsic factors driving account success. These analytics help companies identify the best salespeople and allocate them to their most important accounts. Analysis can also reveal the statistically important traits of high-performing salespeople, which improves both hiring and people development.
Taking it a step further, some companies are integrating email, calendar, and CRM interaction data to identify which actions in the field correlate with success, particularly for technical sellers whose value is harder to assess. One organization used the data-science firm QuantumBlack1 to discover which behaviors of presales experts correlated with deal wins and productivity. Based on those findings, it was able to train these presales experts accordingly and deploy them to maximize their value. Combined with predictive pipeline management, this reduced the cost of sales by 6 percent and boosted revenue by 2 percent.
3. Maximize customer lifetime value. Companies that have complex product portfolios can find it tough to match solutions to specific customer needs. Salespeople rely on simple decision rules but this still requires time-consuming interactions and often leads to missed opportunities to sell related items.
Many B2B companies are implementing next-product-to-buy algorithms that draw on data about what similar customers have bought. A logistics company mined such historical ordering patterns to identify cross-sell opportunities within its customer base and then built tailored microcampaigns around those opportunities. Simply by identifying underserved customers, the company boosted revenues fivefold for its pilot products.
The approach also helps retain customers. Engaging customers at risk of leaving for a competitor requires recognizing the signs of customer discontent well before they take action. These types of problems are perfectly suited to the pattern-recognition skills of machine-learning algorithms. The marketing-analytics team at a global chemicals company, for instance, wanted to reduce its SME customer churn. The team built a predictive model based on more than 30 variables and identified ten key factors that pushed customers away. It also realized with a shock that its most important 15 percent of customers were actually three times more likely to purchase elsewhere than other customers were. Another key finding was that the more products a customer had, the less likely they were to leave. Cross-selling mattered and was a stronger driver of customer loyalty than price changes. Each regional sales manager swiftly found a list of at‐risk customers on his or her desk with guidance on how to engage each one to ensure they stayed loyal. Armed with these insights, the company reduced churn by 25 percent.
4. Get the right price. In the opaque world of B2B price negotiations, deal analytics can provide price transparency and allow sellers to make complex trade-offs during negotiations. Traditionally, B2B sellers have relied heavily on experience to guide their pricing decisions. But purchasing teams got smart and started deploying their own sophisticated pricing tools, which put sales teams on the back foot.
Dynamic deal scoring relevels the playing field by placing relevant deal information in the hands of sales reps during the negotiation. Using decision-tree analytics, reps can identify similar purchases and comparable deal information to guide selling. Customers with similar pricing behavior are clustered based on factors such as industry vertical, past purchase behavior, or size. One software company was able to increase return on sales by more than 20 percent by providing pricing information based on statistically similar deals to the field.
A second challenge is setting the price of new products or solutions, particularly when there is no comparable product on the market or market conditions shift rapidly. Companies are implementing dynamic-pricing engines that integrate real-time competitive and market data with sales strategies to generate optimal quotes. One online media company used dynamic pricing to generate real-time quotes for classified space and was able to generate 5 percent more revenue. By embedding the analytics within a test-and-learn approach, it continued to improve and reap the benefits of higher pricing, greater volume, and increased customer satisfaction.
Similarly, a software firm tested more than 20 different combinations of price and value propositions, and found, surprisingly, that to maximize revenue it needed to raise prices. Although this move cut the number of potential sales by 10 percent, it grew the average size of each sale by 25 percent, leading to an overall increase in revenue.
Implementation: Capturing the benefits of analytics at scale
Implementing an effective analytics program is notoriously tricky. While some companies have struggled with execution, others have been deterred from even getting started because of their infrastructure or a lack of the right talent. At the same time, overworked sales leaders can have difficulty evaluating and prioritizing various analytics initiatives when confronted with an array of complex analytics options.
We have found that companies are most successful when they focus on extracting the full value from a limited number of use cases rather than trying to implement a broad-based analytics transformation. Successfully identifying the best use cases requires analysis of both financial impact and feasibility. It’s important, however, not to get caught up in endless rounds of analysis. Quick and dirty analysis will often surface the best options to start with, though additional work may be necessary to evaluate trickier issues like scale and security.
In our experience, successful companies take five specific actions to overcome the most significant common obstacles (Exhibit 3).
First, they recognize that perfect data do not exist. Yet, by cleverly implementing machine-learning approaches and complementing internal data with external sources, leading companies have been able to extract valuable insights even from poor data. Over time, data quality improves as positive early results justify greater investment in data infrastructure and quality.
Second, they build data-analytics talent, but they don’t forget the importance of field insights in the analytics engine. This means hiring people with advanced skills in statistics and machine learning but complementing them with experienced sales-analytics experts who can translate the insights into actions for the field.
Third, they use low-cost solutions to get started. Many leading solutions are relatively inexpensive and ready to deploy from the cloud. Further investment may be needed in the future, especially in data infrastructure, but like greater investment in data quality, it can be done once the business value of the analytics is clear.
Fourth, they embed analytics within defined sales workflows to ensure insights are available at the time they are most valuable, e.g., integrating deal-scoring algorithms into sales tools and related processes, such as deal approvals, so salespeople can use that information during negotiations. However, very often this means integration into legacy CRM or marketing systems. Too often this integration is considered too late or not at all, which either means the analytics delivers a one-time benefit only, or—worse—the insights never make it the last mile to the front line.
Finally, if insights are to drive action, they must be accompanied by change management in the form of clear communication, incentives, training, and performance management, or salespeople will just ignore them.
Once slow-moving and driven by intuition, data and new analytical techniques have introduced greater rigor, efficiency, and insight. In many industries, it is the adoption of advanced analytics that has begun to differentiate the winners from the rest.