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Strengthening Predictive Analytics with Intent Data

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mrpfdAs sales and marketing strategies are increasingly driven by machine learning and AI, executives in these areas should be looking for the best ways to source data for applications leveraging this technology. One such solution is predictive analytics. The keys to predictive analytics success can be summed in three areas – the quality of the data sources leveraged, the machine learning and AI used to prioritize those data sources, and the actions taken on the results.

Given the foundational importance of data sources to predictive success, intent data is a critical tool for sales and marketers to strengthen a powerful, evidence-based predictive strategy. A single intent source can contain some very actionable insights, but there can be challenges in finding those insights through all of the noise that’s not relevant to your target market.

At the same time, single intent sources don’t always give broad enough coverage of the target market. The real value of intent sources is how they can contribute to a larger, holistic predictive strategy.

 A complete predictive analytics solution combines a sound understanding of your target market and multiple sources of intent data and real-time engagement data to accurately predict and target new accounts. Target market data includes current customer intelligence and lookalike modeling, plus firmographic data derived from organization characteristics and technographic data that looks at organizations’ current solutions to glean information about purchase behavior. Real-time engagement data comes from responses to various sales and marketing tactics, including direct mail, display advertisements, inside and field sales outreach, and email campaigns to help round the solution out.

A Powerful Component for Predictive

 Intent data can build on target market intelligence with first-party and third-party data by helping uncover the content research and engagement trends for solutions in your stack. This type of data includes first-party data such as website traffic monitoring that companies can already access internally, and can be an invaluable advantage for a predictive solution. True intent data incorporates third-party data such as intelligence from the B2B web, making it even more powerful as a contributor to a predictive strategy.

Combined, this internal and external intent data provides a framework from which sales and marketing teams can begin to characterize the accounts that make up their current and prospective customers. Intent data forms part of a solid groundwork from which a predictive customer acquisition strategy can build if it has broad coverage of the target market. Real-time engagement provides the final piece to a true predictive solution.

Closing the Loop

Target market data combined with a single intent data source can offer a good picture of where demand might lie, but the real questions are how accurate and broad that picture is and how quickly you can act on it and learn from those actions. A complete predictive analytics solution looks at a variety of intent sources as well as other sources of intelligence in driving the machine learning algorithm to predict the next best targets.

In addition, real-time engagement offers an opportunity to learn even more about prospects. As customers respond to these marketing and sales tactics, the machine learning feedback loop can ingest that intelligence and add it to the growing image of target accounts. Intent data, coupled with target market data and real-time engagement, provides a clear view of where there is demand and how to best reach those accounts.

Stronger Together 

Intent data is a very powerful, raw tool that can provide great value for sales and marketing teams by identifying net new accounts in an active buying cycle or by helping to prioritize active buying accounts already in a sales database. However, it works best when it can cover most of your target market and is most powerful when used as a pillar of a predictive strategy.

Likewise, a predictive platform that doesn’t tap into intent data is missing a fundamental source of intelligence. A predictive engine without intent data can help determine lookalikes in your target market, but intent data shows the active research and customer engagement that signals a shift in the buyer journey and alerts sales and marketing teams when and how to engage. When a predictive platform is able to receive these signals in real time and act on them before the competition does, that is a true advantage in a competitive marketplace.

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  1. […] because, whenever it comes to shopping, every consumer wants to feel that they’re unique. So, by strengthening predictive analytics, businesses can foresee what the customer’s needs and wants […]

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