3 Reasons why B2B Marketers should use Predictive Analytics in 2018

Risk is part of business.

But what if there were a surefire way to limit (maybe even sometimes nullify) the risks in yours?

That’s probably one of the greatest things about Predictive Analytics. From applications in HR and hiring, logistics and inventory and of course Sales and Marketing efforts, predictive analytics can deliver business impact  in every function of a B2B enterprise. 
 

Julie Lyle, CMO Advisor at DemandJump shares, “In many cases, the B2C market is more attuned to focus resources on day-to-day brand and reputation management. Often in B2B environments, brand management and consumer sentiment monitoring are relegated to only after a crisis strikes. In today’s volatile world, where all customers have instant access to social influencers that are hungry for a compelling story, wise B2B marketers should leverage AI and predictive analytics for continuous real-time sentiment analysis for brand management, crisis management, marketing reputation management during periods of large volumes of social traffic, social activists and influencers. In addition, every marketer, regardless of their vertical, has a fiduciary responsibility to ensure their marketing investments deliver the greatest ROI for their companies. Sophisticated marketing mix modelling also ensures B2B brands reach their targeted buyers at the right place, and the right time, to stay relevant and deliver a better customer experience. Marketers should use marketing mix modelling, powered by AI and predictive analytics, to streamline budgeting and planning processes and allocate spend based on contribution of investments to performance objectives.”

Here are 3 reasons why every B2B marketer should leverage predictive analyticsOpens a new window more in 2018.

1 – Audience Segmentation for Ideal Account Selection

Predictive models can help businesses properly identify, focus on and grow their most profitable customers.

Each business can have specific customer behavior models and algorithms developed for each situation and persona identified. Sales teams can then identify the best accounts to select for different lead nurturing methods like outbound calling or for specific, integrated demand generation programs, setting the exercise up for a higher possibility of success.  In short, Predictive Analytics can help ensure the right sales resources are used at the right time to pursue the right prospects thereby narrowing down the process further. Besides allowing room for a thorough audience segmentation, predictive tools can break down data to foretell churn and send signals out in time. Predictive Analytics technology makes sense of the disparate data and makes each record more complete and detailed. This, in turn, helps analytics to build more robust, holistic ‘personas’, and segments. 

2 – Improved Sales Performance and Forecasting

Predictive analytics can unearth actionable insights that can reduce ‘lost’ sales. According to CSO Insights, approximately 54% of all forecasted deals by sellers don’t make it to the finish line.

Deals can often get blocked at different stages in the funnel and overworked Sales teams may lose sight of these leads that once showed potential. Predictive algorithms make use of internal (from the CRM or Marketing Automation data) and external data sources to spot correlations and predict outcomes to enable marketers to know which prospect will be more likely to close a deal faster Predictive analytics also leverages algorithms to assess several other factors (even Location, Weather, Income, others) that influence a customer’s decision to buy. This can further help Sales teams to know who they should speak to next, what they should say to the lead, how they should communicate, when they should attempt to cross and upsell etc. among several other key pointers. In all, Predictive models helps Sales teams have an edge over lead scoring and prioritization, sales processes and forecasts. 

3 – Optimize Marketing Campaigns

Marketers can plan more fruitful campaigns simply because predictive analytics help determine future customer responses or purchases based on past behavior and modeling patterns. Knowing how your consumer is going to behave or the intent they may have a month from now can help preempt interests or desire to purchase and match it with the right kind of messaging, via the right channel or touchpoints, at the right time in the buyers journey.

Dana Gibber, COO and Co-Founder of Headliner Labs adds, “When making the decision to incorporate predictive analytics into your organization, it’s incredibly helpful to have a sense of how much weight you’ll place on the data. In some instances it’s best to use predictive analytics as one of several factors in making a decision, whereas in others such analytics should be given greater weight. For example, if you use predictive analytics to determine the best locations to open new stores, there are numerous other variables that should come into play; but if you’re creating a product recommendation algorithm, it’s often successful to implement an entirely data-driven process.”

Brian Byer, VP Business Development, Blue Fountain Media contributes, “The ability to use analytics to maximize ROI within your current technology stack is the most important variable.  If you can’t incorporate location level reporting with your global tools, it renders the tools to be nothing more than a fancy Magic 8 Ball. I would recommend planning to incorporate predictive analytics from day one of your digital strategy.  Intelligence is imperative in business and spending money blindly is unwise.  Using the right tools that will enable you to leverage data as you begin to gather a large enough data set to make it actionable is much easier than trying to reverse engineer a solution into a legacy tech stack.  Plan to succeed and it will make incorporating predictive analytics and other new technologies much easier.”

Julie adds, “Regardless of how powerful your predictive analytics technology may be, you first need good data in order to extract any value. If you put garbage in, you’re going to get garbage out. Be wary that different types of math must be applied to different types of business problems. We often hear the term “artificial intelligence” applied as a blanket solution. This is inaccurate. The many types of AI are as diverse as the problems they solve. B2B marketers need to apply the right math to the right problems in order to make strides towards a real solution. Artificial intelligence cannot replace human intellect, strategic thinking, and the importance of day-to-day monitoring, analysis and the resulting impact of the decisions being made. The best marketers will use AI to expand and enhance their capabilities, but they will not be replaced by machines. With or without AI, the days of “set-it-and-forget-it” marketing are over. With the explosion of tools, data sets, competitors and marketing channels, it is more critical now than ever before to monitor continuously, adapt to market shifts and seasonality, and be nimble enough to meet the ever-increasing consumer demands.”

Joe Camacho, CMO, Sabio Mobile says, “It is important to balance predictive analytic with human common sense.  Because machine learning algorithms don’t always have access to “intent” as defined by a human, a machine may predictively optimize a campaign leading to un-intended results.  For example, if we ask the machine to optimize a campaign for clicks, it may inadvertently favor fraudulent traffic with high click rates.  The trick is to have a well balanced approach, where machine and human are continually involved as a check against results that are too good to be true, or counter to the advertiser’s goals”, while Jeremy Fain, CEO and founder, Cognitiv opines, “having a clean, strong signal to train your predictive models off of is key.  In digital marketing, this is not always available or noise has been inserted into the data because of various intricacies of the internet.  This can be cured by sticking to things that only humans can do – like buy things, visit a store, or fill out complex forms.  The data also must be discrete.  This means, unfortunately, that you cannot train an algorithm on aggregate data, such as total number of clicks or a brand affinity report.”

To directly impact your bottom line in 2018, predictive marketing analytics should drive your marketing strategy. And since now is the time of data-driven everything, adding a smart layer like predictive analytics can give your business the ‘complete’ package.

Updated on 21-December-2017