The solutions organizations deploy that use machine learning continuously learn from big data and optimize recommendations in real-time for sales teams. A recent study published by MIT Sloan Management Review reveals that 76% of early adopters are targeting higher sales growth with machine learning. Machine learning is changing the sales game and more importantly, answering the question why—why am I behind? Why is my forecast heading off track? Why isn’t this opportunity closing?
Higher Accuracy Forecasts
It’s Wednesday morning and the first thing I do is log in to my dashboard and review my team’s metrics. Looking at the sales charts, it’s clear that the forecast is heading off track, but the question that is more important for me to answer and address is why is it heading off track? Once I know why I am heading off track, then I need to know how to fix it.
Sales forecasting that uses machine learning techniques draws data from all historical sales forecasts and creates a model that shows a typical path for a successful sale, from start to close, and then compares it to current performance. Anomalies and an off track forecast can be quickly detected in the data, which gives sales leaders the opportunity to step in and redirect sales.
The following example shows a forecast that is slightly above (10% or more) of the forecast. The green dot indicates that the forecast does not fall within the normal forecast trajectory range, as it is outside the main shaded areas. There are still 29 days left in the quarter. Management can continue to monitor the green dot and see whether it falls into normal trajectory as the quarter continues, and they also have enough time to prepare an action plan if it doesn’t.
This approach shows me where I am and what I need to achieve my targets. The patterns in the data show me how things have changed and armed with this knowledge, a roadmap can be created to enact change. The outcome is a sales team that is more proactive and anticipates outcomes based on data, not just assumptions or biases.
Due to the collection of large amounts of data, salespeople and sales leaders have more information than ever before at their disposal about the purchasing and behavioral patterns of customers. The patterns that emerge give sales new insight into customer behavior that was previously hidden.
When it comes to prospecting, knowing how buyers behave is incredibly valuable information. Machine learning can analyze data about leads that have historically converted and build a profile or a lead scoring system. Let’s illustrate this with an example.
Based on historical data, it typically takes 50 days to close a deal and the current deal I’m trying to close has already taken 75 days. The system measures different factors and figures out which factors combined together indicate risk. In this scenario, it is taking longer than usual to close the opportunity and therefore, the risk level is high with every day that passes. The opportunity has been flagged as high-risk in the system and I can then decide whether it is worth pursuing and including in the forecast. The system does the work and I can then decide how to measure and mitigate risk at scale. Without a proper system in place, it is too difficult to do this manually because the interaction of factors is difficult to see as a human.
Better prospecting allows sales teams to make use of their time more efficiently, waste less time on leads that are not likely to convert and to bring us to the next point—fill the pipeline with more high-quality leads.
Fill the Pipeline with High-Quality Opportunities
Applying data-oriented weighting to sales deals, such a lead scoring, gives sales teams a data-driven approach to make decisions and decide what opportunities are worth pursuing and which aren’t. This proves to be beneficial because resources can be allocated for more likely to close deals, and salespeople aren’t wasting their own time chasing deals that aren’t going to close. Of course, this isn’t about reducing the number of opportunities in the pipeline, but rather to fill the pipeline with high-quality opportunities that have been weighted and can be considered more predictable.
Improve Cross-sell and Upsell
Customers have profiles in an organization’s CRM, or where they store their customer data. Every time new information is added these profiles change. When machine learning is applied to profiles, a model can be created whereby new prospects are matched to customers with similar profiles and the models are used to predict new buyer behavior. Machine learning not only predicts what products prospects will purchase, but also their level of investment and the order in which they are most likely to purchase for cross-sell and upsell opportunities. It can cost five times as much to attract a new customer than to keep an existing one and the probability of selling to an existing customer is 60-70% versus the probability of selling to a new prospect is 5-20%.
Sales can then test and experiment whether or not the right offerings are in place and what type of actions can be taken to lead to revenue growth. The outcome for sales is a more efficient and systematic sales process where the discussion with prospects immediately focuses on areas that are most likely to be relevant to them.
Smarter Sales Coaching
We’ve talked a lot about how machine learning can identify patterns and help the sales function target leads more efficiently, which is incredibly insightful information, but machine learning can also improve how management monitors and coaches the sales team.
Keeping track of the entire sales team and monitoring their daily activities and progress can be tricky, but when salesperson historical performance and patterns are available, easy to access and compare, this gives management transparency into the workings of their team. A manager’s dashboard should have a scorecard that lays out the important metrics to track to understand salesperson effectiveness and highlight who is falling behind and who is leading. With this type of information readily available, the data can be used for learning purposes and target who needs extra coaching and more importantly, explain why they may need extra coaching.
Vortini tracks a variety of sales metrics from individual salespeople to different national and international locations. It’s easy to track and compare performance and see who is on top and who is falling behind. The example below compares metrics across various locations in the U.S.A.
When sales leaders take a proactive approach to sales coaching, it also shows to the salesperson that the concerns that are being brought forward to help them improve are backed up by data, rather than other subjective measures.
Enhanced Selling Equals Enhanced Customer Satisfaction
Not only is it the case that your sales function will sell smarter, but the customer base will also be more satisfied. In a recent study, 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%. Keeping your customer base happy is one of the primary goals of the sales function and machine learning won’t replace the human interaction when it comes to building relationships with customers. Having said that, smart machines can be seen as the newly trusted sidekicks in the sales department to provide data and offer analysis to inform data-driven decision making and planning.
Sales teams will make smarter selling decisions, predict which deals are more likely to close, accurately identify prospects worth pursuing and spend more of their time on valuable customer-facing activities. All of these enhanced activities improve overall sales effectiveness and drive growth in an organization. At Vortini, our mission is to help sales teams improve sales effectiveness and drive growth across the organization. Contact us to learn more.
Jess is a communications professional and Vortini’s lead content/web developer. Her current interests lie in the intersection of sales technology and machine learning. In her free time she reads a book-a-week, practices yoga, and is an avid gardener.