Just as digital technologies have improved the array of customer data available to marketers, sale teams have also benefited from advances in analytics technology.
It’s now possible to get a detailed view of potential customers, their previous interactions with your company and what they’re interested in, before the sales team even make contact.
Leads can be accurately qualified and scored to ensure companies deliver the right message at the right time.
But are sales teams making the most of the analytics available to them?
Predictive analytics is more advanced than simple lead scoring and has a greater impact on helping companies close new business.
When used correctly it enables businesses to prioritize sales leads, nurture lukewarm contacts, determine which products a prospect is most likely to be interested in, and implement more accurate sales forecasting.
The importance of analytics for sales teams is one topic that will be open for discussion at Econsultancy and IBM’s BusinessConnect 2015 events in March.
This series of interactive roundtables – hosted in Malaysia, Thailand and Singapore – allow you to join the dialogue with your peers and cross-industry experts to gather marketing insights on how organisations are transforming their respective businesses for the digital age with enterprise analytics.
Implementing predictive analytics
The implementation of predictive analytics is a complicated process that involves a lot of different data and variables, including CRM data, company history (both yours and your clients’), marketing activity, previous sales data and more.
Thankfully a lot of the hard work is automated by SaaS systems, though it does also require the skills of a data scientist to make the most of the analytics tools.
Equally, the quality of the outcome can only ever be as good as the quality of the data available, but that’s a whole separate topic for debate.
Ultimately the outcome from using predictive analytics is not just a set of prioritized leads.
Sales teams also have important information on the accounts and greater context around each prospect, which reduces the need for additional research and improves conversion rates.
An ebook published by IBM describes how its sales teams make use of propensity-to-buy models, which evolve continuously as more data becomes available.
The propensity-to-buy models will not only predict when and what a client may be interested in but their behaviours as well (such as who their key influencers are and what information they needed for a decision).
It’s worth mentioning the cultural barriers that need to be overcome before sales teams can properly make use of analytics.
People at all levels of the company need to be convinced of the benefits of digital transformation, and commercial departments are no different.
At IBM the prevailing attitude among sales leaders was initially one of scepticism. They believed that converting an opportunity into a sale was largely a function of the seller’s actions and could not be predicted in advance.
But the reverse is now true, and having proved to the sales teams that predictive analytics can actually make their jobs easier, IBM reports that its sales managers now commonly request additional analytics tools.
To request for your exclusive invite to attend the Econsultancy/IBM BusinessConnect 2015 events, sign up here: