Selecting the best accounts for Europe-wide ABM program
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Working with a global cloud security vendor, we designed and deployed an ABM program to drive increased sales opportunities in target accounts. By combining firmographic, technographic and behavioural data, we created an account selection and prioritisation methodology.
Our client is a global cloud provider and has a niche product with a long sales cycle and many stakeholders involved in the decision-making process. It would require an army of sales people to generate and manage opportunities, which our client didn’t have. They needed a way to narrow their focus by identifying the accounts with the greatest propensity to buy their products and services from a potential pool of over 3000 companies in Europe. By analysing their data at a contact and account level, and by combining firmographic, technographic and behavioural data, we created an account selection and prioritisation methodology.
This provided a way to look deeper, not only to companies which might be a good fit, but who were also already actively researching potential solutions.
Identifying best-fit accounts without a degree in data science
Our client operates in a market where there are a number of high-value target accounts, and where the sales cycle is long, usually involving many stakeholders in the decision-making process. They are relatively new entrants to the European region and had limited historical sales data to identify common characteristics of the best-fit accounts. They needed a simple, flexible way to select the best accounts now, without extra software or a degree in data science, as well as identifying longer term enhancements such as predictive analytics tools.
A joined-up, deeper understanding of their customers
Through conversations with the regional sales directors, we took their input about what they believed was the ideal customer profile and started to interrogate various systems, including SFDC, Marketo and third-party data, to determine available and accessible variables for modelling. The data could be grouped into the following types:
- Technographics: Install information, including competitive products and products with known gaps which our client’s solution could address
- Firmographics: Size, industry, region, turnover
- Account information: Tier, region, contacts, level of engagement, customer status, lifetime value
In design of the data model, we needed to allow for flexibility, such as regional differences, so we applied different weightings to each variable and created a model which could be fine-tuned as required. We then categorised the variables and converted them into a simple 2×2 matrix, onto which we plotted 3000 accounts as follows:
- Account attractiveness: A combination of firmographics and technographics that allowed us to focus on accounts that were most attractive
- Business Strength & relationship: Indicators that demonstrated how well positioned our client was to win an account, including reach, engagement and relevant experience.
We also created a visual ‘bubble chart’ to make it easier to interpret the data and share the resulting clusters with the regional sales teams.
Building on this knowledge, we then added in purchase intent data to identify which accounts were in an active buyer’s journey and provide a much narrower focus for activity.
Combining technographic, firmographic and account data with intent data is a powerful approach to determine which prospects have a propensity to buy – and when.
A fast, flexible approach to selecting accounts most likely to convert into customers
What started as a daunting task for our client, confused about the array of predictive analytics tools and knowledge to effectively use them, turned into a fast to implement, practical short-term solution to selecting best-fit accounts that were most likely to convert into customers.
In addition, the process we delivered allowed them to have a longer term understanding of their future requirements and what tools they might need to support them.
With readily available data, some experience with Excel or Google Sheets, and an eye for interpreting results, adopting a manual approach to selecting best-fit accounts is fast and flexible, and gets you started on your ABM journey.
If you have any comments or would like to talk to us about how to select the best accounts for your ABM program, we’d be delighted to hear from you. Watch out for our step-by-step guide to using account intelligence to selecting ABM accounts.