How to bring a predictive element into a data-first B2B sales and marketing strategy
As published by B2B News Network
To succeed and stay ahead of the curve in today’s business environment, organizations need seamless ways of compiling, organizing and operationalizing data across multiple systems. The past decade has witnessed sales and marketing technology proliferate throughout organizations. Each piece of sales and marketing technology houses critical signals about target accounts and their buyer journey, but much of this data is underutilized.
The opportunity for forward-looking organizations goes beyond data extraction and aggregation from ERP, CRM and marketing automation platforms. Most sophisticated sales and marketing operations have dozens of pieces of technology built into their daily workflow. Organizations that can capitalize on the data from each of these tools by pulling it all into a central repository and building machine learning models powered by this data will have a competitive advantage. Those that cannot incorporate a predictive element into their customer engagement strategies will lose the opportunity to make the most of data they already hold, ultimately falling behind the competition.
More Data Sources Mean Smarter Predictions
Unifying all the data signals into one location and fully capitalizing on the data’s potential through machine learning and AI is still relatively uncharted territory. In the next two to three years, organizations will continue to curate additional data sources from all of these sales and marketing systems, plus additional third-party sources, to achieve even higher levels of correlation and, therefore, more confidence in the predictions.
With more data, sales and marketing teams can more confidently identify the right target accounts. Additionally, more data means more insight on the customer journey, and the best channel and messaging to reach those target accounts at just the right time.
When working with predictive analytics as part of a sales and marketing strategy, there’s no such thing as too much data. Feeding a predictive engine with more data means smarter, more trustworthy predictions and more complete visibility into an account’s intentions, as well as the size and structure of their buying units.
Driving Sales in New Verticals
Expanding into new verticals presents a prime opportunity for an organization to begin to experiment with new technologies, tools and, most importantly, data sources to help pinpoint key moment of influence.
Successful marketers know that leaning on data offers the best way into new audiences. But when venturing into new territory, marketers often lack the historical, firmographic and lookalike data to understand the new target audience, oftentimes because of outdated legacy systems in place. Key data that would inform the algorithm and prompt it to produce similar accounts might not exist.
While existing datasets for a new vertical may leave something to be desired, that doesn’t mean it’s time to resort back to outdated and unreliable contact lists as a starting point. Even with minimal data on the audience, predictive engines can help form the foundation of a zoomed-in, highly relevant target account list.
Use keywords to begin building out a target audience. Take a look at which accounts have searched for these specific keywords to form a broad base of target accounts. From there, layer on additional filters stemming from experience, knowledge and research about what types of accounts will be a fit.
Overcoming Roadblocks to Success
Becoming a data-first enterprise doesn’t happen overnight. A commitment to leading with data frankly requires upfront investment of time and money. There are many short-term windfalls that come from predictive analytics, and even more valuable results happen when predictive data becomes part of the daily workflow for sales and marketing over the long term. Transitioning to an approach that leads with data is a long journey with milestones along the way.
Marketing technology moves quickly, so one of the biggest challenges when adopting a data-driven strategy is interlacing new predictive technology with legacy systems. The bright and shiny technology investment from a few years ago may already be obsolete, but large enterprises rarely have the flexibility to completely scrap their existing technology for the next new thing. The key is finding ways to leverage existing data sources while incorporating predictive elements.
Many predictive tools can integrate with existing technology, meaning legacy technology investments don’t have to be for naught. Rather than abandoning legacy technology, find predictive tools that work together with the existing data and technology to drive even smarter predictions.
At the end of the day, enterprises that want to continue to grow and scale for success have no choice but to adopt a data-first approach. Data and predictive analytics allow for a completely new way of doing business that goes beyond generating leads. Companies that will continue to prosper must embrace the power of data.