Maximising Sales: Data Science and Sales
You can't do today's job with yesterday's methods and be in business tomorrow
How do you define selling? A lot of people think of selling as persuading/convincing people to buy things they may or may not want or need. To some, selling is all about closing a deal. Thinking of selling in these terms is not very empowering. Frankly, if one has this perspective on selling, it's no wonder one would hate it. However, the process of sales does not mean selling alone. Consider this; a cobbler sitting at a posh location may have to pay Rs 200/- to seek permission for the same while he might sit for free at some ‘not so busy’ area. It’s up to him to analyze if the juice is worth the squeeze and that if the profits outdo the costs incurred. In simple terms, a sales process is a systematic approach involving a series of steps that enables a sales force to close more deals, increase margins and make more sales through referrals.
The 'series of steps' are customer-centric and help the sales force of a company to retain customers and increase sales volume as well as revenues. The 'series of steps' are systematic and not haphazard. Random acts produce random and uncertain results. In sales, random acts can be used occasionally, but a systematic and well-defined best practices approach can assure predictable results. Every sale has five basic obstacles: no need, no money, no hurry, no desire, and no trust.
Whether an organization is a Fortune 500 or a small business, it can often improve its selling process dramatically by shifting away from revenue selling, and focusing instead on profit selling. One of the ways this can be accomplished is by changing the way organizations view sales figures. But first, the organization needs a good measure of sales analytics which provides a richer, more dimensional view into the business. What and who really drives the business? The initial findings after looking through such robust lenses can be eye-opening.
For example, it may be apparent that it is a particular product line that is really driving revenue in a particular region. However, upon drilling down, one may discover it is really a particular sales team, selling a small set of products, to a couple or accounts that are responsible for this product line momentum. The next question an analyst may ask is, are these figures really driving profit margins, or just revenue? And suddenly, this train of thought, this debate about what is driving sales and profits, becomes very specific.
Sales analytics helps develop a ad-hoc ability which reduces reporting times, resulting in more time to sell, and in better focused selling. For example an organization with a $50 million account may set a strategic goal to increase margins, but the field needs to execute this strategy tactically: one account at a time. Without the power of analytics, this may be a daunting task. How cans a sales department micro-manage profitability, without visibility?
This capability is not just for managers: Salespeople can also see the profitability levels by product, by customer, and by market. So now, they can focus on more than just sales volume and price. With this information, they can put more thought around the product mix being pitched relative to the customer type, thus maximizing profit in a particular sales cycle.
All in all, sales analytics represents a major step forward in the move to a profit-centric business model. And this is just from the sales rep's side of the table. Sales analytics also adds tremendous value to the solutions-based selling process--i.e. building a customer-centric business model.
When revenues are down, profitability will suffer and new customer acquisitions may have slowed to a trickle, and customer retention is in a downward spiral. Pressure is up on sales managers to buck the trend and increase sales productivity. At the same time, sales departments are experiencing increases in the volume and complexity of sales information that resides in multiple sales force automation (SFA) and customer relationship management (CRM) applications. Sales and customer relationship data has grown over the past few years to become one of your most valuable productivity assets— if only you could turn it into actionable customer intelligence. Analytic applications to optimize sales are emerging as potent weapons that can provide the competitive edge a company needs to survive and prosper. These targeted software packages pull together information from multiple sales sources to give sales teams consolidated visibility into how to:
Improve sales effectiveness, productivity, and cycle times
Interpret key sales drivers, trends, and issues
Provide sales with a single view of the customer
Optimize marketing-to-sales lead management
EXAMPLE FROM THE INDUSTRY
IBM SALES ANALYTICS
Cognos Customer Performance Sales Analytics unlocks information in CRM and ERP systems to help analyze pipeline and sales force productivity, and accelerate sales cycles. It enables sales team to understand performance, pursue opportunities and accelerate pipeline with up-to-the-minute insight.
CAREER OPPORTUNITIES AND HOW ATI CAN HELP
At ATI, we believe that sales is not only about having a sales process in place, just like simply buying and installing exercising equipment doesn't lead to a chiseled body. Proper use makes the difference. Actively using and a desire to become willing to implement analytics driven sales can work wonders. Tracking sales performance by key metrics is a crucial first step towards improving sales effectiveness and productivity. The metrics available in an analytic application (as well as trigger-driven alerts on problems or anomalies) enable managers to work proactively and respond to issues as they develop—a big step towards shortening sales cycles. They monitor such points as:
Key changes in forecasts and their projected impact on quarterly results
Pipeline analysis by sales stage
Sales trends and revenue fluctuations
Sales performance gains and losses by sales reps, products, and key customers
Actual sales productivity vs. goals