We are all dealing with drastic changes to customer behaviors, attitudes, and buying habits. There are 5 areas you should analyze to develop a customer-centric sales strategy and set your sales team up for success.
Too often the metric for success in sales comes back to what and if you are selling. The problem with this approach is that these backwards looking metrics don’t provide enough context to anticipate the changing needs of your customers.
Nothing underscores this point more than how businesses were able to adapt (or not) to changing customer buying habits changed when COVID-19 hit.
To develop a customer-centric sales strategy, you must look at the motivations behind purchasing decisions.
What if, prior to COVID, you:
Would this knowledge have changed your sales approach when COVID hit and purchasing habits changed?
There’s a difference between typical sales reporting (what sold?) and true sales analytics (what sells?). Sales analytics will help you answer the questions above and uncover what techniques produce sales.
At Analytics8 we think a successful sales strategy leverages data from 5 key areas: customer profiles, customer behavior, pipeline accuracy, sales performance, and sales targeting.
1.) Customer Profiles
Customers often share similar characteristics but have small (and important) nuances. It’s important to look at as much data as possible about your customer to understand these nuances to know what to sell, when to sell, and who to sell to.
This following image is a screenshot from our internal sales dashboard which tells us when a customer spent money, what they bought, and how lucrative they are.
Analytics8 Sales Dashboard (client names removed): Where customers spent money, what they bought, and how lucrative they are
A robust customer profile will show what solutions they are warm to, which saves sales reps time and adds social capital with the customer.
2.) Customer Behavior
Collecting data and profiling customers is a good start but machine learning and advanced analytics help capture deeper insights about customer behavior that the human brain alone can’t extract.
Machine learning is based on data inputs in the form of features, such as customer traits, product traits, or usage patterns. A machine learning model can programmatically analyze these features to identify relevant data points like importance and influence factors or anomalies that may not have been readily apparent.
Machine learning can be applied to sales data to perform more accurate sales forecasting, improve marketing efforts, better predict customer churn, and so much more.
High-level overview of how machine learning might work to predict customer churn
3.) Pipeline Accuracy
Too often, sales reps look at previous year sales, check their gut, and assume the business will continue to grow because the pipeline looks full. But when buying habits change across the globe like they did this past year with COVID-19,growth is far from certain. By using data to understand the reasons why customers bought in the first place, sales teams can more effectively adjust projections when motivations and/or circumstances change.
The following image is a screenshot of sheet in our sales dashboard shows accounts that we’ve worked within the last 6 months who paid invoices on time and filters out accounts with overdue invoices. That tells us which accounts have a budget, a need, authority, and a timeline; all powerful indicators of continued future work.
Analytics8 Sales Dashboard: Accounts within the last 6 months who paid invoices on time
The following sheet shows the pipeline of customers with which we have a high confidence in. Based on the data, these customers are likely to result in closed deals and generate income before end of year.
Analytics8 Sales Dashboard: Pipeline of customers with high confidence indicators of closing
By looking at the most resilient customers, we overcome the inherent fragility of pipeline accuracy; and forecasting is built on data-driven experience instead of hopeful thinking.
4.) Sales Performance
Sales metrics often include: Who’s selling more? Which location or salesperson is making their targets? How many calls are they making and emails are they sending?
These are all good metrics to track, but Sales Analytics should answer much more: what techniques are producing sales?
The following table shows different ways to measure how much each salesperson sold and provides a view of their activities to see what may drive sales.
Analytics8 Sales Dashboard: What activities drive sales?
The following scatter plot compares the amount sold to profit gained among the salespeople, demonstrating what and who drives the most profit.
Analytics8 Sales Dashboard: scatter plot comparing the amount sold to profit gained among salespeople
Both dashboards provide much more context about what actually drives sales.
5.) Sales Targeting
Who should salespeople target? When is the right time to target them, and how can we improve our chances of uncovering real prospects?
In an ideal world, we’d have a 1-to-1 customer relationship, but that’s usually not feasible. Customer segmentation and interpretable machine learning models help identify the right messaging for the right customers at the right time.
But it’s very important to frequently revisit segmentation models. New customers may not be similar to existing customers, and as we’ve discussed, existing customers’ needs, preferences, and behaviors are in flux. If you’re not constantly refining and updating your segmentation based upon new data and new assumptions about the world around us, all your sales and marketing personalization efforts will be in vain.
To get more tips on how to use your sales data to develop a customer-centric sales approach, listen to the full webinar recording.
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