The future of sales rests in understanding what customers want and need better than they know themselves. Not only is this possible, it is expected.
In last week’s blog post, I explained the challenger sales model. This blog focuses on how we can use information to deliver our challenger sales model at the best possible time to each and every customer and prospect.
A proactive mission:
Serving the customer is a proactive mission, and data is the fuel. For example:
- We can use LinkedIn to know when a buyer has changed jobs.
- Every time a restaurant changes its menu, this is trackable online, especially through Twitter.
- Today we are able to tell who is on our own website as well as what they are looking at and when they are actually looking at it.
When we combine this type of data with the information we already have regarding what our customers are buying from us and when they are buying, it moves us into a whole new realm. Basically, this is the future of sales â€“ now.
The future of sales â€“ now:
The future of sales is not simply about contacting a customer when it is time to reorder; it is about:
- timing activities else based on when the customer actually needs to reorder
- sending a customised newsletter to each customer on the day that will most likely lead to a purchase, rather than sending a generic newsletter on the first of the month
- knowing when a buyer has changed jobs and then reaching out to the buyer on the seventh day of their new job, since we already know that is the day the buyer will most likely be looking for new products
- calling our top customers at the very moment that they are browsing our website looking at our product
These are just four examples of how we can understand the customer story better; data can tell us how we should act. That is the future of sales.
Analytics in action:
The image above shows three types of business analytics.
The most common and well-known form of analytics is descriptive. This describes what has happened or what is happening. You can use site visitation data such as Google Analytics to reveal the number of visitors to your website, what brought them there and what they found most interesting. Descriptive analytics also comes in the form of customer information stored in your CRM system or database. You can calculate your biggest and/or most profitable customers from these records of past events.
Predictive analytics calculates likely activities to come, using data mining to answer the questions, â€œWhat will happen?â€ and â€œWhat might happen?â€ For example, predictive analytics might be used to increase temporary floor and customer service staff at a retail establishment, during the week following Black Friday, based on an established percentage of returns and exchange requests derived from previous years’ data. Predictive analytics has a strong ability to identify patterns lurking within static, descriptive data.
Prescriptive Analytics uses data intelligence to suggest new activities, such as challenging a client to open a new line of business or to offer some new menu items, based on current ordering patterns. An increase in orders for spicy sauces and condiments might signal changing tastes of the clientele, which should be acted upon.
Making this a reality:
Bringing the future of sales to Qualifirst in 2015 is a key part of our strategy for growth and I have communicated the importance of this policy to my staff nationwide. By sharing this vision we can make it a reality.