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How B2B Sales & Marketing Leaders Can Leverage Advanced & Predictive Analytics To Accelerate Growth


B2C marketers have up to this point been the unquestionable leaders when it comes to using data science and predictive analytics to better understand their customers and drive growth. And while such large volumes of customer and transactional data are not often present in B2B, there still lies a tremendous opportunity for B2B sales and marketing leaders to leverage data science and predictive analytics to achieve the same outcome. However, capitalizing on this opportunity requires a shift in mindset to a data first approach to driving growth.


Adopting a data-centric approach to sales and marketing and implementing the necessary tools and processes that support advanced analytics means B2B sales and marketing teams can significantly improve their organization’s ability to acquire, grow, and retain customers. A well-run advanced analytics program can impact marketing’s ability to;


Increase lead volume: Identifying companies that have a higher likelihood of engaging and eventually purchasing as well as understanding what messaging will be most effective when targeting those companies lead volume from demand gen. activities will increase.


Increase customer lifetime value: By developing a data-driven profile that identifies companies that have a higher likelihood of becoming high value customers marketing teams can invest more in acquiring those companies.


Improve messaging and the ability to deliver greater personalization: Using predictive models to identify customer segments allows marketers to create better segmentation that is backed by data not hunches. And follow on market research across those segments is what provides the ability to understand the varying needs and business drivers across each of those different segments. The end result is messaging and buyer journeys that will resonate more with buyer personas in those segments.


Increase expansion revenue: Using predictive analytics to make better cross-sell and upsell recommendations and subsequently drive expansion revenue with customers is something B2C companies have capitalized on for quite some time. For B2B companies with more complex product offerings or a large volume of SKUs the same opportunity exists. By analyzing past purchases and the behavior of existing customers and looking at what solutions and SKUs different segments of companies purchase throughout the customer lifecycle, predictions can be made about what new customers in those segments will be more likely to buy. These insights will naturally allow sales, marketing, and customer support to make better upsell and cross-sell recommendations.


Reduce customer acquisition cost: By using the insights predictive analytics provides, companies can not only redirect budget and resources towards the accounts that are more likely to become customers but also increase opportunity win rates. These outputs will in turn yield a lower customer acquisition cost.


Identify new market opportunities: Predictive analytics and machine learning algorithms can be used to develop lookalike models that identify data patterns across a company’s existing customers and subsequently expand and improve their target market by finding new accounts that exhibit similar attributes.


Start with data infrastructure


Implementing a data science program first requires a strong data infrastructure. This is something that is often lacking across many B2B sales and marketing teams. But without it, the path to using data and analytics to accelerate growth becomes a lot less feasible. Building the right data infrastructure can be a daunting task and requires several key elements;


  1. Establish effective data governance

  2. Consolidate sales and marketing data into a central location that becomes the “single source of truth”.

  3. Standardize and cleanse existing data

  4. Create a stronger data profile by augmenting existing customer data with 3rd-party data.


Identify and Build Models That Will Have The Most Impact


Not all predictive analytics models are a good fit for every marketing team. The ability to understand what types of models can be developed, the insights and business outcomes they can provide, and most importantly the applicability or lack thereof within the context of a company’s environment is imperative.


Factors such as the amount of historical data that is in place, the complexity of the product / solution offering, size of the target market, and price point, all weigh heavily in determining what models will have more or less impact, or are even feasible. Data science talent alone is not sufficient in determining the right models to build an effective advanced analytics program. There must be resources that understand how and when to use data science to achieve specific outcomes within a particular business domain (in this case sales and marketing).


Use Change Management To Ensure Adoption and Success


Using advanced and predictive analytics to guide both marketing strategy and tactical execution requires an organizational shift in mindset. And in order to derive maximum value from this, there must be an effective change management effort. It can mean the difference between a program that yields a high return on investment and accelerates growth, or one that fails to achieve the core objectives.


This is not a process that needs to be overly complicated, but definitely should not be ignored. The right approach to change management should be comprised of three key elements;


1. Build a compelling business case: This is an obvious variable and by taking a data-centric approach allows for a much more compelling business case


2. Create alignment and obtain the necessary buy-in: Another obvious component, but what’s also important here is that there is buy-in and adoption for business leaders not just in marketing, but also in sales, account management, and even the product team. As the outputs of an advanced analytics program will positively impact these business units as well.


3. Take smaller more manageable steps: There is no need to build and implement an entire program overnight. Taking smaller, more manageable steps allows you to measure, refine, and drive change in a more systematic way.


4. Ensure action is taken: The most well built, sophisticated models mean nothing if revenue leaders don’t use these insights to change their strategic approach and behaviors. Disrupting the status-quo always presents a significant challenge, but will pay sizable dividends over the long-term.


Produce Measurable Outcomes


Building an advanced analytics strategy and program that accelerates revenue growth and generates a strong ROI requires a significant investment of time and resources but the payback will be tremendous. The goal for every organization should be to not only accelerate revenue growth, but also create a force multiplier across marketing, sales, and customer success that enables them to be far more effective with the same amount of effort and resources.


Taking a data-centric approach to generating growth is really all about taking a customer-centric approach. The insights that can be gleaned from data and predictive analytics are essentially telling us how our customers and prospects buy, what they prefer, and what it takes to acquire them, and keep them as happy, successful customers. The B2B sales and marketing teams that realize success comes from taking a customer first approach and not a product first approach will be the most successful. And those who leverage advanced analytics to drive that customer first approach will not only create an incredibly strong competitive advantage, but change the growth trajectory of their organization.


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