Accounts Based Marketing or ABM is an end-to-end marketing focusing on turning particular contacts within an account into potential revenue-generating customers with the help of sales-marketing alignment. It requires a careful analysis and filtering of the available data to be able to sift through all the accounts and zero-in on the list of potential accounts and then potential prospects within those accounts. This is called Predictive Analytics for Account Based Marketing.
Even though most B2B marketers have accepted the realtime advantages of ABM over traditional marketing, ABM is still in its initial years of both maturity and adoption from the strategic as well as the tactical viewpoint.
According to the ITSMA research, over 50% of ABM programs have been in play for less than a year and just 17% have been running for three years or more.
However, given that ABM delivers on an average double the ROI of traditional marketing, its adoption will take wings and is predicted to skyrocket. The biggest challenge that the marketers and strategists face in this is the implementation of ABM which requires personalization, budget allocation and a thorough analysis on a more professional and productive platform. For this predictive analytics, data sifting and intent data are utilized.
Introduction to Predictive Analytics
Predictive Analytics is a study and analysis of data using various advanced techniques and tools like data mining, statistics, machine learning, modeling, etc to make predictions about the market trends in the future.
The biggest challenge for sales and marketing teams are the sub-standard erratic and often incomplete company and prospect data. Sorting through incomplete forms, questionnaires, anonymous web visits, and event attendees is time-consuming and most of the time useless endeavor. Read more
Database efficiency is sadly overlooked and underestimated by a majority of companies and businesses. This hampers their growth by making it difficult to grow into new segments, channels, and technologies. It is because the cornerstone of any sales and marketing strategy is good productive efficient data.
Superior data and insights not only help but also improve the chances of targeting accounts and prospects that are best suited for the company by their sales and marketing representatives. Without predictive analytics, this whole process of zeroing in on potential accounts can not only be time consuming but also a daunting and difficult process.
Information technology, management and business practices are all combined using analytical tools like data mining and predictive modeling to project and make future trend prognosis. The transactional and historical data trends are studied to identify future opportunities and risks.
ABM hinges on proficient data management and analysis. The whole ABM strategy can only succeed if the data pertaining to management, accounts, business practices etc is carefully interpreted, analyzed and utilized.
Predictive Analytics model assess risks assigning scores or ranks under a linear set of conditions by investigating and breaking down the relationships among different factors. Hence, huge amounts of data can be successfully interpreted by the company by using predictive analytics.
Both structured and unstructured data can be studied using data mining, statistics and text analytics to uncover patterns and relationships allowing organizations to proactively and intelligently anticipate behaviors and outcomes based on data not assumptions.
The Process of Predictive Analytics
Predictive Analytics is an automated process that mechanizes tradeoffs and complex decisions to not only make predictions but also track changing events to update its recommendations to fully take advantage of its predictions. The process involves:
- Project definition and data collection: Project objectives, scope, and data sets are first defined then data is prepared and collected from multiple sources like questionnaires, web traffic, online forms, etc.
- Data analysis using statistics and predictive modeling: The collected data is then analyzed using standard statistical and predictive tools like hypothesis testing to arrive at conclusions and validate assumptions taking into account multilevel model evaluations.
- Model operation and observation: Lastly, the validated assumptions are deployed in the operations to create strategies aimed at garnering optimal performance and results.
Predictive Analytics adoption in ABM
ABM is preferred over traditional B2B marketing by organizations that are forward-thinking visionaries that focus their attention on long term result-oriented targeting. The standard ABM analysis and strategy is taken a step further by the Predictive Analytics by allowing the marketer’s insights into the real-time data projections.
The positive relation between ABM and predictive analytics resulting in the high ROI over the years have made organizations across the world take cognizance of the affirmative reinforcing relationship shared by the two.
The adoption of Predictive Analytics into the ABM structure requires some initial points to create a lucrative base:
- The organization is ABM-centered and content marketers have started applying ABM strategy.
- The marketers and sales representatives understand and are prepared for predictive insights and its implementation into the strategy.
- Due importance is accorded to marketing and sales alignment by both the teams.
- Directors understand and are keen to see the predictive analytics for account based marketing implemented on ground level.
However, it needs to be noted that for a smooth ABM predictive analytics approach from aforementioned base, the company needs to have a solid targeted ABM strategy already in place. This includes having a master target account list which includes the thousands or tens of thousands collected accounts of the company that it hopes to target.
Next, this targeted list has to be segregated into grades usually based on the firmographic qualifications. Also each account has to be graded according to its future prospects. Then the marketers need to work on the individual contact insights. They need to have a comprehensive understanding of the prospects. Once the ABM timeline involves these elements it can incorporate the predictive analytics effectively.
It is important that all the concerned staff understands and accepts the changes that predictive analysis will bring in the system through strategy development changes, past strategy evaluations and current strategy reiterations. They need to be prepared technically and the company must have an organization shift to incorporate the same.
Other Factors Involved
Once the base for Predictive Analytics adoption into the ABM is set we need to look at other factors.
1. There has to be a marketing mechanization program in the organization which is well integrated with its backend technologies including its CRM.
Data planning, hygiene and marketing development is more defined with integration across the company’s technologies.
It is also suggested that the company’s marketing team makes use of the optimization solutions like content platforms, A/B testing etc to enhance the ABM engagement process for a predictive approach.
2. Another factor to take into effect is the incorporation of CRM with the attribution solutions within the company. Studying the marketing initiatives that actually get converted into the revenue-generating assets forms an integral part of the ABM predictive analytics adoption. Both offline and online data through all marketing channels needs to be both tracked and synced with the company’s CRM using the attribution solutions enabling an inclusive display of all marketing key points within the same CRM.
Attribution solutions are responsible for displaying lead-to-account mapping and showcasing marketing key points as per individual accounts. This enables more effective strategy development since all the contacts are already tracked and mapped across all channels into respective accounts.
Hence attribution solution automatically predisposes ABM towards predictive ABM analytics since the data tracking and lead-to-account mapping is already done.
Predictive Analytics Applications and Advantages in ABM
Various segments are involved in the predictive analytics application including CRM, fraud detection, collection analytics, risk management, underwriting and direct marketing to name a few.
CRM objectives like marketing campaigns, customer services etc are achieved by using the predictive analytics approach.
Further, future risks involved in any particular strategy can be forecasted using predictive analytical tools like statics growth and an appropriate risk management plan can be implemented.
Potential risk behavioral outline of a customer can also be calculated by using relevant data to minimize its effects and underwriting the quantities accordingly.
Predictive Analytics boosts the Account Based Marketing strategy primarily through the following avenues:
- Improved account rating and priority: ABM hinges around named accounts, its prospects, and timing. The marketing representatives need to be on top of things at all times to nail the exact moment when the prospect can be turned into the revenue-generating asset. Mis-timing can result in huge lost opportunities. The predictive analytics helps by giving real-time data, insights and forecasts into the most opportune moment for the conversion of prospects optimizing both the strategy and budget allocation.
- Content Personalization: Most companies face content personalization issues since the data they base it on is actually account intelligence based on numerical data. Predictive analytics helps by going beyond these numbers and forecasting buyer behavior giving the marketers a tool to content personalization as per individual prospects.
- Precise objective scoring: As the accounts and prospects near the end of the funnel, predictive analytics operates by forecasting the best time for sales overview bypassing several risks like outsmarting competitors and data deletion.
Are You Ready To Adopt Predictive Analytics for your ABM?
Even though the benefits of Account Based Marketing and predictive ABM analytics are very impressive, most companies fall short of effectively implementing the same. It usually happens because unlocking the potential requires an understanding of both the ABM and its AI.
It is here that most B2B marketers fail.
It is therefore imperative to employ and invest in an intent data set that is based on predictive analytics and AI learning to reap the benefits of high-quality productive data without the hassle of perceiving the underlying technologies.