How predictive analytics in ABM can help the sales team to build their pipeline
For generations marketers have been practicing “fishing with the net” philosophy, where the demand team focuses on attracting large chunks of the audience, hoping to retain a few as customers. But, as time has passed that net has been replaced by a modern spear, which means, focusing on a big account as a customer with the technique called Account-based marketing (ABM).
What makes ABM a smarter strategy for the sales team?
Creates an efficient and optimized sales funnel: ABM focuses on channels that have high chances of generating revenue, which means marketing efforts and resource waste is limited. It narrows the focus of the organization to accounts that are more likely to pay.
Sales and Marketing becomes one team: As this system focuses on accounts instead of just leads, the efforts of the marketing and sales department are aligned. They are both focused on working and engaging with customers that are more likely to buy instead of just generating the lead.
Customer Experience: Strategies built with ABM are customer-driven, they are built on insights specific to the account, it’s content and is highly personalized. It focuses on the convenience of decision-makers of the accounts rather than the marketer. The personal touch that comes with ABM is the key to better customer experience.
According to Engagio, 97% of marketers who have tried Account-Based Marketing experienced a higher return on investment than any other strategy.
Though the concept of ABM was developed in 2004, B2B businesses have only started to practice it for the past five years. It implies that ABM is still in its initial years of adoption and maturity from a strategic point of view. According to research by ITSMA, more than 50% of ABM programs that have been developed are not even a year old, only 17% of having been in play for three years or more.
Even though ABM is said to expand its wing in the coming years, the biggest challenges that marketing strategists face are implementation, personalization, budget allocation, and proper channel of analysis. This is where Predictive analysis comes in place.
Predictive analysis in ABM
Predictive analysis or analytics is the study and use of data, machine learning techniques, and statistical algorithms to analyze the past and current trends for making future predictions. It is a combination of techniques and tools like data mining and predictive modeling etc. to provide an insight into what can happen in the near future.
ABM is an end-to-end marketing solution that focuses on a limited number of prospects that can or can not turn into potential customers. It requires careful filtering and analysis of data to list all potential prospects. This is called predictive analysis for Account-Based Marketing.
Process of Predictive analysis
Predictive Analysis program for account-based marketing compiles and compares the data to prepare a list of target accounts and use it to identify and target new accounts. This process uses AI that can identify valuable prospects that manual researching and scoring couldn’t have found easily. This automated process involves:
STEP 1: Data collection and defining project
Firstly, project goals, scope, and data sets are defined. Then both primary and secondary data is collected from web traffic, insights, already existing databases, and offline forms, etc.
STEP 2: Data analysis using statistical approach and predictive modeling
The collected data is analyzed using predictive and statistical tools to make conclusions and validate presuppositions through a multi-level approach model.
STEP 3: Model operation and observation
In the end, these validations are brought into play to create strategies that aim to garner optimal performance and results.
Such analytical models only work when B2B marketers have an ABM plan ready at hand, these models help them analyze the possible deviations and help minimize risk.
Advantages of Predictive Analysis
By leveraging AI tools, any business can boost ABM performance. It’s key benefits are:
Prospect prioritization: Timing plays a crucial role in ABM. Even if one has a list of possible prospects, it can go in vain if they aren’t targeted on time, hence, resulting in a low conversion rate and wastage of funds. Predictive insights provide real-time intelligence of accounts. Therefore, it can be prioritized and targeted.
Deeper Content Personalisation: The key feature of ABM is that it is a personalized approach. Predictive insights analyses the buying behavior, consumer expectations, and purchasing power, etc. this would help them deeply curate personalized content optimally.
A large chunk of data can be studied: One of the key benefits is that both structured and unstructured data can be studied using data mining, text analytics, and statistics. It easily unravels the pattern and relationships that allow organizations to intelligently anticipate the changing behavior and outcomes that are based on facts and not assumptions.
Precise objective scoring: When the prospects are near the end of the funnel, predictive analytics forecasts the best time for sales overview that bypasses several risks like outsmarting competitors and data deletion.
The purpose of ABM is to shift from generating voluminous leads to tactics that are designed to cater to the needs of an individual income-generating account. Doing so requires a core understanding of both accounts and individuals, predictive analysis makes it easier for businesses to assess this information.
Account-Based Marketing hinges on proficient data management and analysis. An ABM strategy can only succeed if the data pertaining to accounts, management, and business practices is carefully summarised, analyzed, and utilized. Predictive Analytics models assess risks assigning scores or ranks by a linear set of conditions for investigating and breaking down the relationships among different factors. Hence, a huge amount of data can be successfully decoded by the company by using predictive analytics.