Sales forecasting is a barometer for business performance. It reflects how well a business serves its market and is critical to long-term growth. However, the majority (69%) of companies’ efforts to predict sales falls short. The mix of methodologies used, which range from in-depth historical analyses to gut feeling assessments, have weaknesses.
Weighted pipeline forecasting is the most common method and employs a linear model. It involves assigning a percentage likelihood of a deal closing to every sales opportunity in the pipeline and multiplying it by the revenue value expected. The sales forecast is the aggregate sum of all revenue values.
But selling is a zero-sum game, which is something the weighted pipeline fails to address. For example, if a $100,000 deal is given a conversion rate of 25% given its stage in the sales funnel, its value is $25,000. In most cases, that deal rarely results in $25,000 in revenue.
The rhythm of small and medium-sized businesses can be disturbed when sales forecasts are off. This is equally true when sales fail to materialise or when more sales are converted than expected. Analysing forecast performance can reveal issues in selling, personnel or negotiating, which, when addressed, will help improve forecasting and improve business operations.
Artificial Intelligence is often touted as a cure-all solution for business problems. It isn’t. However, when applied to sales forecasting it offers a significant increase in performance. Presently, Amazon offer Amazon Forecast which uses Machine Learning (ML) for forecasting.
Microsoft offer their Sales Forecasting module within Microsoft Dynamics. Both tools offer a general solution that is applicable to many businesses while Impact Tech have developed Impact AI specifically to serve brokerages.
Better sales forecasting increases growth
Research shows that companies are better at growing their year-over-year revenue when their sales forecasting performance is higher. They also hit quota more often. A significant part of improving sales forecasting depends on lead scoring. The criteria used should reflect the likelihood a deal will close within a defined time-frame.
Lead scoring errors are often a result of misleading buying signals. A 2016 study reveals that it is a well-recognised problem. 61% of companies highlighted “misleading buying signals” as the biggest challenge in lead scoring. The study also reveals only 40% of sales teams believe lead scoring adds value even though it is used by most sales teams.
Artificial intelligence increases sales forecasting performance through its ability to analyse large volumes of real-time and historical data to identify the best leads. It is commonly referred to as automated lead scoring and delivers better results because it employs sophisticated non-linear models which are both interpretable and explainable.
The latest lead scoring system developed by Impact Tech uses Ensemble Learning in combination with ML and Continuous Learning to reveal the best Leads by drilling deep into demographic, firmographic, and technographic data.
Recent research suggests that less than half of all forecasted sales opportunities are converted. Ensemble Learning methods use multiple learning algorithms to drive a better predictive performance than is possible from any of the constituent ML algorithms alone.
When combined with Continual Learning, it enables autonomous incremental development of ever more complex skills and knowledge. When applied to automated lead scoring, it results in a higher percentage of conversions because more effort is focused on the best sales opportunities.
As your business grows, your lead scoring needs to scale to ensure your agents give the highest priority to the most important leads. AI-driven automated lead scoring performs this function.
Ultimately, automated lead scoring combined with automated lead segmentation will result in the most suitable agents focusing on the best leads. They will make their approach at the most opportune time while equipped with the most effective sales information.
Responding better to market influences to reduce churn
Existing customers are often the biggest source of revenue for most businesses. They are also the most cost-effective. It is four times more expensive to acquire a new client than it is to upsell to existing customers.
Despite this, too many businesses struggle to pair their products with customer needs. This is partly explained by the fact that over one third fail to track their customers’ journey. Churn inevitably follows as retention teams are unaware of changing customer needs. However, a significant reduction in churn rates is possible through applying AI.
An AI-driven CRM offers insights that enable agents to respond to influences in the market. By tracking the customer journey, agents are aware of the experience – both positive and negative – a client is having in real time.
Armed with insights about sentiment, preferences and other buying triggers, an agent moves from solution selling to insight selling.
Modern businesses are successfully leveraging the benefits of our hyper-connected world. However, this level of connectivity with clients also requires immediate action when a complaint arises.
Research reveals that 25% of customers will not return following just one negative experience. Businesses that use AI-powered chatbots can engage with customers 24/7/365, respond instantly and when it suits the client.
Using a CRM with AI insights to monitor customer activity patterns enables a business to proactively identify issues before they escalate and significantly reduce churn and increase retention.
Customer behaviour, market conditions and competitive forces all impact sales performance but are mostly left unaccounted for by traditional sales forecasting methods that rely on historical sales data and other traditional parameters.
Platforms that use AI to pull together all influencing factors and update their importance through ML enable agents to see the true value of a lead. Despite some unsubstantiated hype, AI’s influence on business is undoubtedly set to grow. The benefits of early adoption for sales forecasting are undisputed as are the consequences of sticking with past methods.
