Ideal Customer Profiles for Seller Enablement
Ideal Customer Profiles for Seller Enablement
Ideal Customer Profiles for Seller Enablement
Ideal Customer Profiles for Seller Enablement
Within large companies, but also for small startups, the questions we need to answer are: To whom do we actually sell our product? And with whom do we need to talk? Who makes the buying decision? Ideal customer profiles help product teams present the correct arguments to the right people, so that the chances of a successful sale increase. Through a study around the IBM Data Fabric offering, we reviewed the go-to market strategy and tested how the messaging resonates with potential customers. This resulted in so-called ideal customer profiles with arguments that focus on their everyday problems and KPIs.
APRIL 2023 | 5 MIN. READ
AUTHOR: Robin Auer, User Reasearch Lead, IBM Data and AI
TEAM: Robin Langerak, Leah Yoon, Kathy Alvero, and Robin Auer
Goal
During the seller enablement process for IBMs Data Fabric offering we realized, that the definition of the market segment of the new offering was too broad. User Research was asked to dig deeper. We wanted to understand our customers' problems and their needs around data, to help internal teams promote Data Fabric in the right places. The goals was to identify the decision maker roles and contacts and the arguments needed to convince them of our offering. Long story short, we wanted to understand how our customers make decisions.
Participants
In total, we conducted 17 interviews with decision-makers and product owners. Of these, 9 were external like existing customers or prospects. 8 were internal customers of IBM's data governance department and others. The external interview partners had to have knowledge of data architecture approaches (either data fabric or data mesh) and be involved in data product buying processes. Roles included: Director of Data Stewardship, Data Governance Manager, Chief Architect, and Senior Technical Program Manager.
Procedure
This process was based on the Grounded Theory concept, which means we combined 2 to 3 studies to answer the research questions and objectives. The first phase began with an internal exploration and discussions with various executive stakeholders on sales and product development.
“This is a best practice in message testing and I strongly recommend that you write down [this team's] names and emails to do this in the future.”
IBM Transformation Leader
In the second step, we conducted a survey of IBM salespeople that we developed in collaboration with the global sales team. This survey is also part of my portfolio and is described in more detail here. The survey had several objectives. We wanted to learn more about the general sales process: Who do they talk to? What are their door openers to get in touch with new customers or stay in touch with existing customers. We were also interested in the current state of Data Fabric, including the difficulty of sales calls. We wanted to understand what information they need to be able to talk about Data Fabric.
Based on the results of the survey and stakeholder discussions, we have selected the right interviewees for qualitative interviews, which brings us to the third step of the study. With the interviews, we wanted to understand the previous findings in more detail, including the customer perspective on Data Fabric. We also wanted to understand better the role of the sales and evaluation process from their perspective.
In the beginning, we had planned a fourth step with a quantitative study. The goal was to get a more comprehensive picture by asking about 100 data specialists from different industries about their maturity level in data analysis. We wanted to understand the reasons why they are not getting their full potential from their data today. Unfortunately, this step did not happen anymore. Based on the finished three phases, we developed the Ideal Customer Profiles for Data Fabric.
“I really appreciate all your hard work. I think your observations are spot on. […] We really need to make sure that we're testing something like this so it's resonating with the people. That's a really good and valuable learning for all of us. […] I think the enablement will be vastly better based on the work that you all have done.”
Senior Vice President IBM Software
Key Insights
As part of my portfolio, I always try to write as much as I can about my processes and methods. In doing so, I intentionally leave out concrete results, as I am not allowed to show them due to confidentiality. This time I can only talk superficially about the key insights. We found that our language was full of internal jargon and which wordings were more appropriate. We were also able to define the specific key performance indicators (KPIs) that our customers use to measure the success of a data management software solution. All this helped our internal teams to adapt the go-to-market strategy and change the wording accordingly.
I talk a bit more about the Survey among Sellers in the other article. But we learned that they did not know enough about customer pain points. As a result, they were not able to sell Data Fabric as offering. Thanks to our interviews, we identified three customer success stories, which went into three main data fabric sales plays. These sales plays became the foundation of IBM's Data Fabric sales strategy. Two of them are online and can be found here:
“We leveraged all of the great feedback to build out the IBM use case materials. It was incredibly helpful and enabled us to better articulate how IBM is leveraging and building its data fabric […].”
IBM Chief Analytics Office
Outcome/Impact
Ideal Customer Profiles are great because they help companies to describe their target audience and focus sales and marketing activities on ideal prospects, instead of wasting time with companies in the wrong market segment. They help to sweep away assumptions and allow us to get to know our customers. If done well, they can be used to refine products, content, and sales funnel which leads to increased engagement with the right audience and more profit. In our case, the ideal customer profiles ended up in three customers success stories, which are still used by the worldwide sales teams at IBM. An ideal customer profile can look like this. As already said, I’m not able to share the actual data here in my portfolio
Industrie
Banking,
SaaS
Geography
North America
Company Size
More than 1.000
Budget
10.000 per month
Buying Process
First eval. w/ experts then POC with prov.
Decision Maker
CDO, CRO,
and CTO
Pain Points
…
…
Goals
Shortterm,
Longterm
Technologies
Privat Cloud,
etc.
Other relevant Attributes
3rd party tools used, critical business goals,
problems they want to solve with our offering, etc.
Disclaimer: This Data is just an example and not a real result from this study.
Images Copyright:
ING carries out its data fabric vision on ibm.com
Data Fabric for the Hybrid Multi Cloud on ibm.com
Head image by DilokaStudio on Freepik