The artificial intelligence of tomorrow promises untold changes. But AI is also well suited to solve these three obvious sales and marketing challenges today.
1. Improving sales yields
Businesses that sell a large range of products to a large range of customers are burdened by an analytical challenge that drags down their yield on sales resources.
The challenge is to help each sales rep answer the following two questions: Who should I call on this week? and What personalised messages/insights should I deliver in each call?
Most reps answer these questions by accessing various systems and databases, sifting through endless customer records, survey info and transactional data. They manually identify the customers to visit, add them to a call plan, optimise the plan for the best logistical sequence, and finally, Google each customer before a visit. This process has nothing to do with sales. It is an analytical task.
Instead of suffering through a manual process, AI can help each rep access a cloud application and ask a machine to display the best opportunities in their territory. How? The machine analyses each customer – including sales trends, complaints, returns and so on – and augments the information with external data in order to make comparisons between volumes, behaviours and opportunities between similar customers and segments. The machine tells the sales rep: Here are the best opportunities right now and Go and see these people and deliver these messages/insights.
2. Dynamic pricing
Just a one percent increase in price can deliver a disproportionate increase in margin and profit. It’s critical for businesses to optimise pricing, particularly in low-growth countries like Australia where opportunities for revenue and market share growth are limited.
By aggregating, overlaying, and analysing various data sets – including sales, market intelligence and competitor information – organisations can optimise pricing strategies and decisions.
The problem lies with the amount of data analysis. Imagine that a business serves 100,000 customers with 100,000 products. Now imagine the difficulty in dynamically determining a margin optimised price for each individual product for each individual customer. Impossible.
But, AI can analyse all of the products, customer types, geography and volume combinations on a regular and ongoing basis, and then dynamically provide each sales rep with “price guides” during the quoting process to maximise margin and increase the odds of a sale.
3. Automating CRM
CRM preserves “corporate memory” with customers. What did we quote? Did the quote convert into an order? Did we deliver on time? Was there a complaint? When was the last visit?
Executives want good activity reporting, but end users must pay for this by keeping the CRM system up to date with endless data entry. That’s the fundamental conflict. While managers and executives need CRM data entry to achieve their objectives, sales reps view CRM entry as a chore that adds no value to their job.
CRM should be “touchless”. It should provide managers with the reporting they need, without burdening end users.
AI can monitor end users as they go about their job – analysing their plans, tasks and executed activities – and automatically update the relevant customer records.
The less time sales reps spend updating their CRM, the greater their compliance will be, which is another way to improve productivity.
There’s no need to look to the future for solutions to these problems. With modern AI, they’re already here.
Matthew Michalewicz (pictured above) is the chief executive of Complexica, which has developed Larry The Digital Analyst to offer solutions for these kinds of business cases. Michalewicz was the closing speaker at CRN Pipeline 2017.