Welcome to part 5 of a 6-part article series on knowledge, the Buyer Journey, marketing/sales synergy and better sales results. Our last article From knowledge analytics to insights – help from machine learning looked at the ways in which machine learning can help to produce more precise knowledge from the data sources you have used. This article connects the dots between the powerful knowledge gained from that…and successful sales.
What would your sales director say if you could save 6 man-months for her team? Or if you could build a shortlist of the 50 targets with the greatest potential of being upsold in the next 30 days? The data your company owns can be converted into knowledge – and more importantly into revenue – together with the help of the sales team. Want to understand how this actually works? See the Visma case in our previous blog.
Most marketers don’t appreciate the reality of salespeople’s revenue-driven lives day-to-day and month-to-month. Reaching sales targets is essentially the only goal that salespeople have. Marketers’ drivers ought to be helping salespeople to close deals and to reduce routine tasks in order to close more deals.
It stands to reason, then, that modern marketers’ key career driver should be the ability to convert customer and prospect data into knowledge in order to drive better sales results. This ability is based on bringing a thorough understanding of customers’ purchasing processes into alignment with sales teams’ fine-tuned procedures in such a way as to close more deals more quickly.
Offer concrete, actionable things
Sales is typically short-sighted. It’s not a weakness; it’s a requirement of success. Reaching sales targets requires wins this month and the next. Then comes the next quarter. With sales, objectives need to be concrete and simple, with a clear focus on what to sell.
A list of people who visit your website regularly is not a prospect list. The top 20 key decision makers with turnover above 250 M€ who have recently shown interest in your product lines X and Y are prospects. You should probably call them. Or the 40 clients worth 2.4 M€ in sales revenue with a 75% probability of cancelling their contract in the next six months. Those are actionable lists that sales can move on immediately.
Picture it. Draw an image of a better opportunity from the dull data. Salespeople read better when sentences contain dollar or euro signs. That’s the nature of the profession.
Be sales’ time salvation
Financial departments first introduced CRM as a means of understanding and forecasting sales. Systems were sold as “sales organisers” to get buy-in. The reporting that CRMs demanded of salespeople stigmatised these systems as reporting torture machines for decades.
What can you do to save sales’ time on routines? Find the right contacts? Provide their contact information? Include prospects’ financial data?
Salesforce’s third annual State of Sales report found that sales professionals spend just 34% of their time selling. The time-eaters that gobble up the other 66% are the ones marketing can help to erase:
- Prioritising leads/opportunities
- Manually entering customer info
- Researching prospects
Marketing efforts don’t end with a campaign launch. Delivering a prioritised list of leads into CRM would be better—and even that is just a milestone on the journey of integrating marketing and sales, where all share the KPI of sales won and lost.
A practical example of this is improving the accuracy of sales forecasts. Machine learning algorithms can run through historical selling, pricing and buying data to produce Marketing-Qualified Lead (MQL) and Sales-Qualified Lead (SQL) estimates that are several degrees more accurate than those of an optimistic sales team. With improved sales accuracy, companies gain the confidence they need to be more forward-looking in their investment plans, with a much lower level of risk.
How to turn marketing data into sales knowledge
Take the latest season’s sales kick-off meeting target list and turn it into marketing goal headlines. “Increase mid-size customer sales by 15% through an expanded service offering” easily turns into a marketing goal. You know the number of mid-sized customers and the total budgetary value of that segment. So you know how much 15% means, on average, and what earnings an expanded service offering typically brings. Sales can give you their rule-of-thumb win rate. Now you know how many leads your marketing operations must produce.
Predicting churn and retention rates is a bit more challenging. But not as far-fetched as it may sound. Modern machine-learning algorithms will work that list with 75% accuracy or greater in no time. That could be the next big win of your career: to derive great benefit from the data your company already owns.
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