Though we covered a lot of ground during the #DataDrivenSummit, the most central takeaway was a rather simple one: everything we do as marketers must be informed by data, even how we define and package our businesses.
More data means:
More targeted insight into what your persona wants
Greater accuracy when defining target markets
A stronger competitive edge
Better performance on all campaigns
When developing your data-driven GTM, it’s important to identify your core competencies, develop your market and buyer definitions, work on your messaging, and package/price appropriately.
Crafting a Go-To-Market (GTM) Strategy Using Data
Beyond understanding your industry and defining criteria for your market, you want to amass data that will uncover who is buying your products and services. More importantly, you want to unearth what motivates their decision making. This means quantifying your buyer personas using — you guessed it — data!
Assigning numeric values to the human experience sounds odd, but it’s quite doable (and very effective) if you leverage a propensity model.
According to Destination CRM, these systems “correlate customer characteristics with anticipated behaviors or propensities… [the] success is underpinned by the quality of your customer data and how effectively it’s segmented.” Propensity models allow marketers to quantify the likelihood that a customer will reference or share marketing materials, buy, and even churn.
Using a propensity model to build a persona usually starts by segmenting your current customers, assigning scores to key characteristics, and then applying that data to a certain industries or markets (and even companies within them). This initial work allows you to gauge how well received your product or service will be in said market or industry. Typically, your findings can be compared to data from different groupings of companies or firms to refine characteristics that make your new targeted personas stand out.
This market data helps inform who you should be targeting (and, most importantly, how.) Before a site visitor or curious consumer ever becomes a lead, you can assess how valuable or high-interest he/she is by aggregating figures around appealing customer segments — demographics, who they work for, their location, available lead scores — and their associated market data.
Applying these models can also help identify which segments are likely to respond to certain messaging, using predictive measures, and even support buyer retention.
Every communication professional knows: your words matter. A lot. One of my favorite quotes from the Data Driven Summit was this: “What you say in advertising is more important than how you say it.”
When thinking about positioning, phrase of your pitch in simple terms:
For (audience), (your product) is a (category name) that provides (main benefit) unlike (primary competitors) which/who provide(s) (their main benefits)
…keeping in mind your persona, their need, and the features and benefits of your product/service. How do you train your sight on just the right messaging for your audience though?
By launching several variations of your messaging, and highlighting different unique selling points or use cases accordingly, you can select which approach will perform the best. This is where data comes in.. again!
Test a message’s effectiveness using conversion rate optimization tests. We like to leverage the A/B methodology at Salted Stone. This allows us to launch at least two versions of copy for our clients, and track how successful each variation was in converting the reader.
PRICING & PACKAGING
Though it’s likely not the most glamorous aspect of your business, packaging and pricing methodologies have an undeniable impact on your attractiveness.
Is your pricing structure competitor-based? If so, consider whether or not they are using an appropriate standard for your market… if your competitors’ pricing is flawed, then yours is now flawed, too.
Value-based systems are generally considered the most accurate estimates of market value. But keep price sensitivity in mind, according to your perceived value, and factor in elasticity by actively surveying your users.
Here’s where data comes in.
If you collect price point sensitivity information from existing customers on a scale of “Too Expensive” to “Too Inexpensive,” it becomes possible to plot these responses and find an equilibrium. If most folks are comfortable with the value of your offering, then you likely won’t need to make changes, but if you fall on the pricey end for the majority of respondents, it may be time to adjust.
This should be done regularly and within specific brackets or ‘tiers’ of users, and may also be paired with a question about likelihood to renew/buy again.