The shift to account-based sales is a multistep journey, and each organization that employs ABC uses a slightly different version of the model
May 17, 2023
Author: Bryan Belanger
May 17, 2023
Author: Bryan Belanger
Account-based sales (ABS) is hardly a new concept. However, account-based sales is surging in popularity, particularly among technology sector players aiming to optimize their outbound efforts among a growing crop of competitive solutions. ABS refers to a go-to-market strategy that aligns an organization’s sales efforts and resources around the pursuit of a set of specific, high-value target accounts, personas and contacts within the company’s ideal customer profile (ICP).
The shift to account-based sales is a multistep journey, and each organization that employs ABC uses a slightly different version of the model. In a blog post, HubSpot recommends the following steps for deploying an account-based sales model. While the list also extends into account-based marketing, we believe it is a comprehensive summary of how to activate ABS. HubSpot describes the steps as follows:
An ABS approach is straightforward in many respects, but it is not easy to deploy. Any sales organization has at least some building blocks already in place to execute on the steps above and begin to pilot account-based selling. But what separates “good” from “great” when it comes to activating an ABS program?
Like most things, it comes down to people, process and technology. ABS requires a high-agency, capable and committed cross-functional team. That team must be aligned to and incented around a clear ABS strategy and managed to fair and consistent key performance indicators. Standard sales processes must be in place for activating ABS efforts. Lastly, selling teams must be enabled by best-in-class technology that helps them save time and optimize workflows at all steps of the process.
We could write a book diving deep into the people, process and technology implications for ABS. But in today’s post, we want to focus on the single-biggest disruptive force impacting those ABS pillars and shaping the future of account-based selling strategies and operations: data.
Data has been a critical driving force in the ascendency of account-based selling. Digitization of commerce, including for B2B products and services, continues to create an unprecedented amount of all types of data. According to Exploding Topics, approximately 330 million terabytes of data are created daily and this figure is expected to grow by more than 150% by 2025. This expands the applicability of account-based selling as a viable strategy, as organizations have more valuable and actionable data to leveraging the sales process. All that data could create overload quickly, however. As a result, an entire ecosystem of technology products and services has emerged to help companies harness and organize data for sales use cases.
Given the size and evolution of the sales data vendor ecosystem, it can be difficult to know where to start when devising a data strategy for your account-based selling efforts. In our experience, it helps to start by distilling “data” as a concept into different types of use cases.
In Figure 1, there are two types of data listed in italics under the Fit and Opportunity use cases: Strategic and operational, with subcategories; and Strategic and operational change.
These aren’t data types that you’ve seen in the platform you currently use for company data, so what do we mean?
You hopefully can envision how this type of data might be used in practice. When scoring and prioritizing accounts into tiers at the Fit stage, this type of second-level filtering can help you optimize the account lists. When you begin executing sales motions against those account lists, this type of data can trigger event-based alerts in your sales process that allow you to fine-tune your messaging and offers to target buying committees at key accounts during their time of need. This can help resolve a pain point during the client outreach process.
We believe these categories of data are extremely valuable to the design and execution of an account-based sales (and/or marketing) program. Yet they are largely nonexistent in the tools that are currently available to sales teams. Why? This type of data is difficult and costly to collect. The account data and research tools on the market today are built for breadth, and gathering and updating strategic and operational data requires depth. Building to depth demands significant internal and/or external efforts that often combine software tools and services (or labor, if internal).
We’re inherently a little biased, but this is where we think research agencies (and/or an internal market research approach and mindset) come into play. Sure, there are “do-it-for-me” agencies to help build and execute account-based programs. TBR has supported these types of projects regularly by building datasets and/or profiles on a set of named client accounts. If you have the budget and appetite for that type of engagement, it can make a lot of sense.
However, we find that most sales leaders we engage with are looking to integrate this type of strategic and operational data into their existing sales technology stack and workflow in an “as a Service” structure. Therefore, we have designed and advocated for a hybrid model that combines research techniques, research automation technologies and existing sales stack tools to help augment your existing account data and convey customer buying signals to your revenue teams in the context of your existing CRM and sales process workflows.
To illustrate how we deploy this into our clients’ sales processes, we’ll walk through an example of how we’re currently supporting chief revenue officers and their teams at pricing-related software and/or pricing consulting companies with our XaaS Pricing data and services.
Step 1: Build ICP list with firmographic and technographic data: We use Crunchbase plus Seamless.ai to build out an initial list of our clients’ ICP accounts based on their submitted ICP details. In some engagements, we enter at the “Defining ICP” stage and can certainly help build out ICPs based on CRM and/or market data. However, for the sake of illustration, we’ll assume our hypothetical client has determined an initial view of ICPs. This ICP list is built around typical firmographic and technographic inputs at this stage. For example, our client might say their ICP is “publicly traded companies in the U.S. with annual revenue of $500 million or greater, with a dedicated market research and/or pricing strategy team, currently selling subscription-based software offerings and using Stripe or a similar platform to offer self-service billing.” We can then build a list of the client’s ICP logos working backward from that ICP statement.
Step 2: Create a data model for strategic and operational data about your ICP accounts: As a sales team, you’re close to your customers, so you already understand how their businesses work. You understand the strategic and operational decisions they need to make to execute their own business models and pursue their customers. You want to translate that knowledge into a data model to fuel your go-to-market efforts. The goal is to translate your clients’ key strategic and operational needs into a structured, consistent set of data, such that you can collect that data across all of your ICP accounts. Then you can compare that data across your accounts to analyze them more deeply for strategic and operational fit with your product or service. Continuing our example from above, let’s say your company provides market research software for conjoint analysis. A key use case for your solution might be “to help companies optimize the features of their subscription product packages.” What type of data would be helpful? You probably want to determine whether each ICP target offers a subscription, whether the ICP offers its solution in multiple packages and, if so, how many packages are offered for each product. You can probably think of 10 or 20 other similar data points that would be valuable as well. The goal is to identify and prioritize all those data points, identify where you will source them, translate them into standard database fields, and establish rules for what data inputs and entry types you’ll allow for each of those fields. Complete this process, and voila! You’ve built a relational database structure for account-based sales data.
Step 3: Collect data: Now that you’ve defined the blueprint, you need to build the house by collecting the actual data. We recommend collecting all the data you’ve defined in your model, even if the data will be used for different purposes. There are a couple of options for collecting this data, and they all involve a bit of “scraping.” To our knowledge, there’s no database out there that has all of this data on hand for all possible product and/or service use cases. You could go to the websites of each ICP account and manually collect the data in raw format and then translate it into your standard database structure as defined in the previous step. You could build a web scraper or use an off-the-shelf web-scraping tool to collect that data (ensuring, of course, you adhere to the Terms of Service of the websites you’re scraping, many of which prevent bot-based scraping). You could use a human-based automated scraping solution such as Amazon Mechanical Turk to scale the data collection manually via a globally distributed team. In the not-so-distant future, you’ll be able to build on top of ChatGPT or other generative AI technologies and/or subscribe to a generative AI tool built specifically for account research to scrape websites and retrieve data to a set of specifications you define. Or you could deploy a combination of these approaches using a technology-supported, human-in-the-loop model, which is what we do.
Step 4: Enrich ICP list with initial strategic and operational data: You’ve collected your additional data, and now it’s time to integrate it with your original ICP data to build your complete account research view. You have your master account list and ICP list from Crunchbase, ZoomInfo or whatever tool you used in Step 1. Now, you need to evaluate the data you gathered in steps 2, 3 and 4, and determine which data will be used as part of your ICP list refinement and which data will be used for ongoing opportunity-based account signaling and campaigning. This doesn’t mean you won’t continue updating and refining all data — you will. But you need to set up some structure. Bringing back our conjoint software example — data that answers questions such as “does the company use a subscription model or not” would likely be used as an additional filter for your ICP targeting since it’s a baseline criterion that defines applicability for your product. You may have collected other data during your efforts that is relevant but less applicable to structuring an ICP. For example, perhaps you scraped data on the total number of subscription products each company uses, how frequently each company changes subscription offerings and pricing, and/or the type of ICPs the companies themselves serve. These are insights that can help you in terms of account outreach personalization and for signal-timed outreach, but they are less likely to be delimiters on your ICP. For example, you probably aren’t going to filter an ICP based on whether a company wants to run conjoint on three or five total products, but if you have a data signal that says a company just increased its total products from three to five by launching two new solutions, that would be a valuable signal to inform outreach.
Step 5: Score, rank, tier and prioritize ICP list accounts: Once you’ve added this layer of data on your ICP accounts, you can combine it with your other data sources to score each account based on the fit with your company’s product and/or service. If you use data sources and/or tech platforms listed in the Intent and Engagement use cases in Figure 1, you can build a weighted score that incorporates scores for each category. Account scoring will yield natural segmentation of your ICP accounts that you can prioritize for your outbound efforts. Here, we defer to our clients, as everyone has different territory designs and approaches for classifying and segmenting accounts. In our engagements, we establish a “fit” score based on a weighted average model that considers account fit based on the filter criteria described earlier. Our clients typically then combine that data and scoring with their own Intent and Engagement platforms to build a complete view of their target accounts. Remember here that this isn’t a static process — depending on your ICP criteria (particularly the strategic and operational filter criteria), account scores may change and accounts may rise and fall in priority based on developments in their businesses. We revisit this by auditing the account scoring monthly and quarterly, as well as in real time as we execute steps 6 and 7.
Step 6: Automate ICP list for high-level and low-level data scanning: Once you have these models built, you want to keep them fresh. You probably want to do that for your full ICP list and for your prioritized list. For your full ICP list, including both prioritized and nonprioritized accounts, you want to track data for major changes. For your prioritized list, you want to track and update your data model with signal changes on a near real-time basis, such that you can alert your reps when opportunities arise. For both of these efforts, we like to use a tool called Visualping, which is software platform that tracks changes on defined webpages and sends alerts via email. If your data model consists of outputs from public sources on the web, you can use Visualping or a similar tool to send alerts when there is a change on the relevant webpage(s) for any of your target accounts. If the data you’re scraping is embedded in social media, job posting sites, PDFs or other less structured resources, you’ll need to get more creative. Periodic manual scanning is always an option. Or you may choose to build your own web-scraping asset. In time, generative AI tools such as Tactic or Browse will prove valuable. We typically cover 20 or 30 metrics on a real-time basis using these methods.
Step 7: Track strategic and operational changes and customer buying signals: Delivery of data updates and signals to our clients’ sales teams depends on their ways of working, but often a monthly cadence of data updates proves sufficient. We track changes, update the data model and deliver an updated output that highlights key changes and preserves historical data. For us as a third party, we share the outputs as a .csv file, and then a sequence of steps is required to translate that output into action. Systems come into play here. Most clients upload this data into Salesforce or whatever CRM they use. There may also be a need to integrate it into other engagement- or account-based selling platforms. The key goal is to get the data to your reps in a timely, consistent fashion and have an established process, such that they can execute on the data efficiently.
Data is increasingly a primary path to differentiation as organizations adopt an account-based selling approach for their technology product and/or services. This has created a burgeoning ecosystem of sales data solutions providers. While this evolution has delivered wide-ranging benefits, it has also created gaps, as most vendors launching sales data tools and technologies have aimed for breadth and scale over depth and value. There is opportunity for sales operations and strategy teams to differentiate from their peers by leveraging these best-of-breed tools. In addition, the tools can be augmented by collecting strategic and operational target account data that can be more effectively used in account prioritization and outreach.
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