In a competitive business market, the importance of the system that maintains and fosters growth for one’s customer base cannot be understated. In 2013, expenditures on customer relationship management software increased by 13 percent to over $20 billion, according to Gartner. The technology is not going away anytime soon: Joanne Correia, research vice president at Gartner, notes, "CRM will be at the heart of digital initiatives in coming years. This is one technology area that will get funding because digital business is critical for companies to remain competitive.” With the amount of resources invested in CRM, a company must examine these systems to ensure it is getting the most out of its investment. There are a number of ways to optimize a CRM implementation, but data analytics in particular offers exciting opportunities to drive innovation in the sales cycle. CRM data analytics can enable a business to make the sales process smarter as well as empower field reps with easy access to business intelligence. This is done not only by studying CRM data itself but also by enriching that data with additional information from both inside and outside the organization.
Start at Account Planning
The basic premise for selling goods and services has always been constant: convert opportunities with new and existing customers into closed deals. But with the evolution of a salesforce and the potential for a national customer base, the complexity of the sales process has exploded. Key sales activities now include territory alignment, customer segmentation and multistage account management. So where do analytics come into play? This begins at the account planning process. Account planning allocates the resources of the sales team to customers and prospects in an efficient manner. CRM analytics can aid this process by bringing together past sales activity data with historical sales results and other external data influencers. Ask any sales team to name its top 10 customers and the answer should come easily. But can they tell you who the top 10 customers will be next quarter? Possibly, though with the right business intelligence models, answering that question becomes easier by providing what-if analyses and projection trends. With this type of information, organizations can then segment customers into prioritized groups.
Build a Better Sales Call
CRM analytics can also be used to ensure that a sales interaction is the best it can be. Building a better sales call depends on the right CRM information architecture. CRM software should not only track accounts and contacts but also the actual sales materials presented to those customers. The advantages this setup brings are many. For instance, sales and marketing materials can be stored centrally and consumed by the field agents as necessary. Cloud-based CRM applications make searching email folders for the latest version of a presentation obsolete. Under this model, the usage and adoption rates of such materials can be tracked in the same way that clicks on a website are analyzed. Additionally, certain regulated industries can automate compliance procedures via digital consent agreements when a customer views certain content.
In the spirit of continuous improvement, feedback for sales presentation materials can be gathered from the customer — either formally or informally — as part of the sales call. That feedback can be stored in the CRM repository to improve future iterations of content. Smarter sales calls are also built by presenting the customer with relevant cross-selling opportunities. Data analytics plays a key role in determining cross-sell chances. For a company with enough order history and customer dimension data, models can be built that will provide an Amazon-type suggestion list of additional products purchased by similar customers. But the most important factor from an analytics perspective is to associate both the sales activities and sales materials with the individual customer. In that way, sales results can be correlated with these factors; insight into what works and what does not becomes readily available.
Incorporate Downstream Data
Data analytics can further enhance CRM by incorporating downstream data back into the application itself. This can take many forms. In an insurance setting, for example, claims data can be incorporated into a customer profile for risk assessment purposes. Or, perhaps there is external data to integrate into a customer profile, which provides indicators that the customer has experienced some significant event and could benefit from a call. For instance, a service vendor may wish to setup alerts for a representative to follow up if news outlets report a customer undergoing a merger, an acquisition or entering a new market. Service or software industries can track usage rates or support requests, making those metrics available within the CRM application. When certain financial KPIs can only be derived downstream from the CRM application, there is no reason why those numbers cannot be fed back into the system to give sales managers a single place to supervise their book of businesses. In that way, the complete picture of an organization’s data can be woven back into this valuable enterprise application.
The future of CRM analytics offers a number of interesting possibilities. Sales force coaching could take on new perspectives if sales presentations were recorded (with consent), and these recordings are submitted to speech-to-text translators. With a large enough sample size, one might be able to analyze the resulting “word clouds” to determine if certain message patterns differentiate a successful call from an unsuccessful one. Richer insights into purchase habits are starting to emerge from the use of guided customer experiences. For instance, imagine if a retail shopping excursion came with the use of a tablet or mobile device to guide the customer’s visit to the store. A virtual assistant is always there to answer questions, understand purchase needs, and make a call to a live associate if necessary. Loyalty program information could be gathered from those who choose to enter it. The result, from a CRM perspective, is a much more individualized experience and a heightened awareness of consumer patterns.
In short, there are many ways the sales process can become smarter through intelligent use of data analytics. Although selling has traditionally been regarded as a soft skill, the post-digital age has provided no shortage of ways sales data can be measured. But the successful organizations will ask — and find — answers to increasingly complex questions: Do certain customers in particular demographic groups exhibit different buying patterns depending on the type of sales interaction they experience? At what level of detail should a customer base be segmented to optimize return? How can sales costs be reduced without negatively impacting financial returns? In order to begin to answer them, one must have access to the right information. And that information is at the heart of CRM analytics and is what makes it such a valuable commodity.