Most principles used in B2B selling today have been in place since the late nineteenth century and coincide with the rise of large mass manufacturing firms. National Cash Register, Westinghouse Electric and others created large, organized sales forces and with them, standardized sales and sales management techniques. It was innovation in technology that drove innovation in sales.
History is now repeating itself. Developments in Analytics technology is driving genuine innovation in the form of predictive sales analytics – a move that is shifting the new normal of what the B2B sales process looks like.
Predictive analytics is an estimated $5 billion market that has seen $1.2 billion in VC funding specific to sales analytics. Startups are sprouting like mushrooms after a fresh rain with “why didn’t I think of that?” applications. The reason? There’s a strong economic case for deploying predictive sales analytics.
Even Salesforce got into the market when they introduced their Wave analytics platform last year. At the time, it was pitched as a platform for large companies to build custom apps that would take data from various sources (not just Salesforce CRM). Last week, they announced Sales Wave Analytics which is a ready-made app that doesn’t require users to develop customized apps on their own.
Here’s the thing though, Sales Wave Analytics, unlike custom apps developed on the Wave analytics platform or many 3rd party, ready-made apps, relies on the data found within your CRM. Solutions that incorporate outside data along with CRM data should provide more accurate insights.
That’s because analytics that rely on your CRM data alone won’t likely give you the full picture and can actually provide misleading insight as a result.
Salesforce users will want to consider 3rd party solutions that take advantage of the Wave analytics platform and apply their own unique data science across a number of sources.
Here are 5 new areas where predictive sales analytics are propelling the industry well into the 21st century.
1) Predictive Forecasting. Forecasts are what drive business planning yet forecast accuracy is a persistent problem. Even with a CRM in place, companies lose significant time creating and rolling-up company-wide sales forecasts. Compounding the problem is that forecasts are based on human judgment—instead of what the data tells us is predictable—resulting in inaccuracies. You can’t optimize operational results if you’re basing decisions on judgment and assumption.
Aviso applies predictive analysis to sales data making it possible to roll-up sales forecasts across an entire organization in minutes. Executives and front-line sales can make better decisions based on hard data.
Clari is also tackling this problem with their own data science and machine learning algorithms that analyze deal activity and changes to CRM to validate forecasted opportunities and identify deals at risk. Clari also analyzes important non-CRM data like emails and calendar activity. That’s important because emails and calendars are a better source of ‘truth’ than is CRM that relies on consistent manual activity logging.
2) Sales Enablement. One benefit of sales analytics technology is that it can facilitate the alignment of sales and marketing (another age-old problem) and fulfill the promise of Sales Enablement. For example, Highspot offers what they call Content Genomics™ a proprietary technology that analyzes the DNA of sales content across the organization. The end result is that marketing has the insight needed to produce content that drives engagement and revenue.
FirstRain was one of the first companies to ride the Salesforce Wave when they introduced Personal Business Analytics for Salesforce1tm. Their solution provides a continuous injection of information that could impact your accounts and deals (opportunities and risks) fine-tuned to your products and services, end markets, and the industries you sell to.
3). Predictive Lead Scoring. Companies in this category aim to identify high-quality, hidden prospects—those that share characteristics of your best customers. This is done by of analyzing a combination of CRM data, campaign results, opportunities won and lost, and Internet data mining.
SalesPredictsolutions uncover insights that simply wouldn’t be possible without data analytics. The system purports to show sales managers who will buy, who will buy more, and who will churn. Using that information, teams can prioritize sales calls based on which leads are most likely to close and improve conversion rates throughout the entire customer lifecycle.
4) Sales Performance Monitoring. Having had a 25 year career in sales, I know that moment all too well when sales projections are submitted and the question is asked, “Are you confident you can get it done?” Of course managers respond with a resolved “YES” even while wondering if they can really make it happen.
Qstream has an interesting approach to the issue of “keeping it real” when assessing true sales capabilities. The company does this by engaging salespeople in fun, scenario-based challenges delivered to their mobile devices. A sophisticated analytics engine takes it from there, providing insights that are specifically actionable, and in turn, helping managers identify knowledge gaps and respond before revenue forecasts are negatively impacted. Gaps in knowledge are closed with tailored coaching and by the system pushing out new challenges fast and efficiently. The platform helps managers proactively identify and adapt to changing market conditions, competitive threats and opportunities.
5) Pricing. What if you can lose fewer deals due to uncompetitive pricing and win more deals at profitable pricing; you’d get bigger revenue and bigger profits. If you have thousands (or millions) of products and as many customers, it’s tough to consistently win more profitable deals. Predictive data and pricing analytics like the one from PROS provide pricing guidance for every deal and reduce the need for “exception approvals” which slow deal momentum.
The above are just a few examples of predictive sales analytics use-cases. The number of solution providers, as I said, is mushrooming and money is pouring in to fertilize the market. GainSight, alone has raised over $50M, while 6sense has received more than $30M in funding. When a market gets crowded you can expect to see consolidation. Indeed, just this month, C9 was scooped up by InsideSales (another predictive analytics company aimed at, you guessed it, inside sales).
The bottom-line is that sales managers need to strip away outdated processes and welcome analytics technology as a way to achieve efficiencies and performance gains. The good news is the sales tech industry is providing a wide range of innovative options. It’s only a matter of time before predictive sales analytics is the new norm.