Harnessing Data-Driven Sales
There was a time when you could set your sales goals based on gut instinct, how sales were last year, and how you thought or hoped sales would be this year. With technology advances, today’s sales executives are making data-driven decisions, using metrics and analytics to guide precision decision-making. Every sales decision needs to be guided using insight gained from analytics, and every sales outcome should be measured and analyzed.
Surprisingly few organizations are able to take advantage of the vast amount of data available to them, such as historical sales performance, seasonal variations, pipeline reviews, and contract renewals. For example, historical data in any organization can be mined to find trends and activity correlations that could improve sales performance. Bringing in additional market conditions, psychographics, and factors using techniques such as big data mining can improve the accuracy of analytics even more, providing metrics that can guide smarter decision-making.
Developing the analytics technology to mine sales data for insight is a discipline in itself, with its own expertise. MarketStar’s data scientists are experts in sales analytics, with years of experience with hundreds of individual sales motions in multiple markets and segments. They mine historical POS data, activity data, and market data so you can benefit from these insights without having to do your own data mining and modeling.
At MarketStar we don’t just recommend the right analytics tools; we have a core competency in setting up and administering data mining and analytics resources. Many of our clients have limited time and resources, so we provide ongoing optimization of these tools on their behalf. We’ve developed a standardized approach to measurement that is transferable and trainable. You get a single source for information, but the best practices for data gathering and analytics also can be shared, along with the intelligence.
Our tech stack provides insight into the end-to-end sales process. We customize the tech stack for each of our customers, using metrics at each point to identify and correct weaknesses and ensure that the sales team is working at its best. We also use the tech stack to refine and share best practices.
Artificial Intelligence (AI) is playing an increasingly important role in sales analytics, just as it is driving greater accuracy in financial planning and supply chain management. We use machine learning to make it easier to assimilate historical data, CRM data, and input about the pipeline in order to come up with more accurate sales performance predictions. The more data you can ingest and analyze, the more accurate sales forecasts will be, including which deals are likely to close when and for how much.
By harnessing analytics through AI and big data techniques, we gain a lot of insight into what drives your sales process, including:
Pacing (i.e., whether you are closing deals fast enough to meet quota)
Whether the pipeline has sufficient leads to produce the revenue to meet quarterly goals
Whether the pipeline is growing at the right rate to meet future goals
A picture of the overall run rate, including revenue from deals that are open and likely to close in the quarter
AI-driven indicators that highlight the potential to win on every deal, with red, yellow, and green alerts to show you where you need to adjust execution to improve the close rate
Smart sequencing (i.e., building sales outreach messaging and cadence based on activity metrics, language usage, and sentiment analysis of sales correspondence)
Once the right big data processes and technology components are in place, forecasting and performing “what if” analytics becomes much easier. Using plug-and-play data reveals different outcomes to inform decisions about changes in staffing and business processes. We rely on our tech stack for ongoing analytics, creating a feedback loop that continues to generate insights into new ways to refine sales processes and continually improve sales performance.