Sales focused organizations have needed for long a transition from assumptions and gut-feelings based decision-making to more strategic and tactical orientation. As Artificial Intelligence gained in popularity, many organizations went over to adopt the technology, but only few were able to reap the benefits promised to them.
Investment in AI is rapidly rising, which has created an AI bubble that is likely to create value but only when used wisely—when you preset the trajectory and put in concerted efforts to reach the target.
Presetting a growth trajectory in sales requires the leadership to consider a number of elements which is significant for the sales success. When that is done, it becomes easier to see where exactly the AI technology fits in the process.
For say, AI has multiple benefits, as in price optimization, identifying up-selling and cross selling potential, managing sales rep productivity and performance, and forecasting. The trajectory you will be setting here must be focused on where your objectives lie.
For each objective, you need to have a separate trajectory, as you would need to feed different data inputs to get the precise result. As it is obvious that you cannot determine the sales rep productivity and optimized rates of products or services with the same set of data.
Let us evaluate the 5 key areas where AI can help to improve sales operations and the elements you need to consider while presetting a successful growth trajectory.
Have you ever experienced choice paralysis?
For example, spending hours deciding on what you are going to eat today. Or the last minute confusion on whether you want to go to the event or not. Choice paralysis in sales is something similar to that where salespersons often feel indecisive about time and preferences to be given to specific leads.
By analysing the historical data on lead behaviour, Artificial Intelligence technology can score the lead based on their likeliness of conversion. It will save a lot of time and energy of salespersons. As they know which lead has maximum chances of conversion, they could spend more time on targeted lead or even devise different strategies for each type of lead.
Here creating the right data points to score a lead is necessary. The ideal data points to measure the quality of lead could be both most recent and the information compiled from the historical data.
Most common data points could be location, income, social media postings, content sent and received during a salesperson interaction journey such as email, voicemail, text messages, and others.
2.Sales Performance Management
Clearly, sales performance can be evaluated from three perspectives:
(i) Individual sales representative performance
(ii) Sales team performance
(iii) Overall sales performance of the organization
AI can help you evaluate the data inputs from all the three perspectives and present a clear picture of performance for each of them. Again the most important task here is to determine the data points on which you want to evaluate the performance.
For example, to evaluate the individual performance, you can evaluate details such as:
- The numbers of calls done
- No. of meeting attended
- No. of follow-up mails sent
- Average conversion rate for each representative
- Lead conversion rate for each representative
- Quota achieved
Although these data sets can help evaluate the performance of individual sales representative but is totally unhelpful if you want to look at the team performance or business performance in terms of revenue generation. For instance, if you want to measure team performance, you might get more value from the data such as:
- Monthly sales growth
- Sales target achieved
- Average purchase value
- Average profit margin
- Sales rep productivity and leaderboard
Similarly, analysing below given data points might help you to make better decisions while making your sales strategies to improve overall sales performance.
- Sales by region
- Sales by contact method
- Customer acquisition cost
- Average conversion time
- Retention and churn rate
The clear notion here is that before AI decides for you, it is you who must decide the parameters on which AI should decide.
The price of a product is something that catches the attention of all the shoppers, whether high, low, or optimal. According to a PwC report, around 60% of shoppers choose companies with optimal prices. But what exactly is the optimal price?
Basically optimal prices are those that do not create a negative effect on shoppers, that do not increase your marginality, and that do not compromise on the sales of other products in the portfolio.
Artificial intelligence can help you fix an optimal price that lets you win the deal and also ensure that you don’t leave any money on the table. AI-led price optimization process includes the evaluation of prices after analyzing various data sets such as customer, channel, point of sales, product portfolio, and stock keeping unit.
Algorithm-powered models can comb a vast amount of pricing related data generated from these sources and recommend a most optimal price for a product. You can take individual, customer-centric decisions such as how much discount you can give to each customer that could give you an edge over competitors.
It is no secret that acquiring a new client is costlier than retaining an existing client. AI-led algorithms can help you bring more business from your existing clients.
The algorithms can evaluate your ERP and CRM sales data to find the likelihood of buying a new or updated product among customers. Based on the information, sales managers and representatives can create targeted offers for individual customers.
Advanced AI-based recommendation engines can help you nurture the first time visitors of your business by sending personalized push notifications and post-purchase emails with product offers to the selected customers.
AI-driven sales forecasting improves the accuracy levels to a great extent. Forecasting is extremely essential how sales operations are managed. Accurate forecasting can help in effective sales management such as hiring and retention of the sales representatives, planning and budgeting, product investments, and marketing. According to SiriusDecisions, 80% of organizations fail to achieve forecasts by 10%.
Using an AI algorithm, sales leaders can predict next quarter’s revenue and take necessary measures to improve or optimize the sales processes.
AI-driven algorithms can scan multiple data points and machine learning scores to generate insight on possible outcomes. With the right data and insight, sales leaders can increase accountability in the team, better organize the resources, and leave nothing for chance.
Putting it all together
You can decode your numbers more accurately with AI. A lot of data is generated while performing day-to-day activities, but not all data is relevant to understand your sales performance. Creating right data to maximum data values is necessary for the AI to help you decode what your number says about your sales operations. Once done, you can streamline your operations and skyrocket your sales.