Today’s sales professionals are faced with critical decisions every day. It’s impossible to predict the future and difficult to choose which new technologies will best help sales teams identify the best prospects to reach out to, what products to offer, and which communication channels will actually get those prospects in a conversation.
There are an utterly overwhelming number of sales tools available and sales managers have the difficult task of not only identifying the top tools, but also guiding their teams through the complicated process of adapting to and fully optimizing those tools to stay competitive.
For 2017, the learning curve is feeling more steep than usual, with top companies throwing words around like ¨AI¨, ¨machine learning¨, and ¨predictive analytics¨, to describe their “must-have” sales tools. Many sales reps are intimidated by how much they don’t know…so what do these fresh buzz words mean, and should companies really be paying attention?
The answer to the above is a resounding YES. Ready or not, machine learning and predictive analytics are already disrupting the sales and marketing industries with Salesforce’s recent State of Marketing report finding 79% of high-performing sales teams are already using some form of predictive analytics. Even the most cautious technology adopters will need to work fast to recognize it’s importance to avoid falling behind.
Let's define these terms in a sales context, and discuss some of their use cases and benefits.
Predictive analytics refers to applications, platforms, and technologies in general, that look at the past behavior of prospects and customers to establish patterns, and use those patterns to “predict” future behaviors.
Machine learning is a type of artificial intelligence (AI) that allows computers to “learn” and adapt programs without direct human input. In a nutshell, computer programs that change when exposed to new data.
Early adopters stand to gain a lot from adapting to the emerging technology, and it is quickly becoming affordable for companies of all sizes. Currently on average, 80% of your sales reps’ time is spent qualifying leads, and only 20% is spent closing. Most labor intensive tasks that make up the qualifying process can be delegated and/or optimized by machines, allowing companies to allocate more of their sales reps’ time to closing deals with interested prospects. The sky's the limit for the capabilities these new technologies will offer, and they are already enabling companies to:
- Eliminate human bias and decrease reliance on intuition and assumptions.
- Leverage website traffic history and interactions to profile prospects, score them, identify the stage of buyer’s journey, and engage in interactive conversations.
- Easily track and gain insight from aggregate data and contact histories, with all interactions and clicks logged.
- Automatically send targeted emails and content offers to leads at optimal times.
- Automate prospecting calls to warm up leads throughout their buyer’s journey and predict the best time to make contact.
- Optimize messaging based on open, click, and call pick up rates and reactions to messaging.
- Anticipate client needs and suggest solutions based on predicted preferences and behaviors.
- Spot unhappy customers through trending social mentions and behaviors.
The automated and data-focused future of sales will reduce the time sales reps spend on repetitive, time consuming prospecting tasks, and increase their available time for personalizing messages and building real relationships with their prospects and customers. Although there is always a period of adjustment, the potential of machine learning and predictive analytics will be decisive in deciding which companies evolve for success, and which fall behind and lag in the past.
If you are interested in machine learning and predictive analytics get in contact with our automation experts and learn how VOIQ's intelligence based platform is advancing sales and marketing.