Evaluating product-market fit is critical for companies. A product that doesn't address customer needs damages the company's reputation and squanders customer trust. Knowledge of customer preferences — in other words, data — can solve the challenges behind determining product-market fit.
However, traditional data cannot uncover edges in a competitive marketplace, and connecting the big data dots can be challenging. After all, if every company in a sector is analyzing and drawing conclusions from the same data, no one gains an advantage.
Given the rising popularity of alternative data sources for business validation, audience research and investment evaluation purposes, it's no surprise that these signals are now being used to optimize product-market fit. Analysts and executive leaders have begun digging beyond surface-level metrics such as overall market size and figuring out customer dynamics that might shape product design.
Here's how alternative datasets are changing the playbook that today's companies use to strategize new products.
Anticipating consumer behavior
Companies have always used data to adopt a proactive stance in the market. The businesses that moved first on data analytics received a boost, and the same pattern is repeating itself with alternative data usage. Customer segmentation is becoming more powerful as companies are discovering new audience metrics that can predict buying behavior.
These new datasets augment traditional metrics like total addressable market (TAM), serviceable addressable market (SAM) and serviceable obtainable market (SOM) to provide more complete pictures of market opportunities.
"When all's said and done, look at the figures you have pulled, and group all your information together to see if it all makes sense," recommends Similarweb's Molly Winik in a recent piece about merging different types of market sizing metrics. "If you have calculated a TAM of $1B, but conversations with business owners in the space point to half that, try to spot where your estimations may have skewed."
Alternative datasets are likewise helping companies zero in on hidden behavioral attributes that reveal product preferences. For instance, alternative data provider AnalyticsIQ developed bespoke customer segments for a travel company looking to offer a new vacation club service.
Using these segments, the travel company refined its product to create personalized experiences. It then implemented tailored ad campaigns that appealed to its audience in more effective ways than traditional ad targeting.
These results are possible only by blending cognitive psychology with data science. Firms in this space gather social media usage, charitable donations, fitness, interests, and recent purchase data to develop intelligent segments.
The result is that companies receive insight into hidden catalysts to product usage. By incorporating these levers into product designs and launch campaigns, companies are more likely to fast-track their product-market fit.
Discover unconventional buyer paths
Can the weather influence how a company's product is viewed and consumed? It's easy to think of a few categories where this is the case. However, companies cannot base new product launches based on unquantifiable insights — they need hard data to justify product investment.
Alternative data sources are revealing previously hidden patterns within buyer journeys that offer companies insight into product demand. While new products face adoption challenges, measuring the potential for success to new audiences is equally challenging.
Residential technology firm Resideo faced this challenge when repositioning itself away from a contractor-focused B2B firm towards a B2C audience. The company needed to understand its new audience beyond surface-level data such as demographics, interests, and favorite channels.
Working with alternative data, Resideo honed in on signals such as geography, weather, and whether the prospect had moved homes recently to gauge product fit and appeal to its new audience. While this campaign was for an existing product, it's easy to see how alternative data can extend itself to uncover buyer paths that conventional datasets miss.
Elsewhere, retailers can leverage mobile phone GPS data to measure foot traffic at a location and correlate this to existing psychographic factors to validate a new product. Brick-and-mortar stores often already leverage foot traffic data to measure success, so enriching existing data with alternative sources that reveal customer proximity is a logical next step.
There isn't a single alternative dataset that magically unlocks buyer behavior. However, the novel viewpoints and correlations that these datasets create in concert with conventional data hold massive potential for companies seeking to launch new products and get inside their customers' minds.
Decipher the ideal marketing language
New products often fail at launch due to inappropriate channel usage. For instance, alternative data provider PlaceIQ highlights a case where a dining chain was keen on expanding its appeal to casual diners. Given the stiff competition in the space, the dining chain had to conduct a deep analysis of customer behavior and tailor its menus to attract this audience.
While surface-level data such as age and income levels narrowed audience sizes down, it didn't reveal much about what customers preferred. PlaceIQ leveraged alternative data such as consumer style preferences and correlated them to visits to competitor dining chains such as Longhorn Steakhouse and Hometown Buffet to build a map.
Along the way, researchers discovered that people who visited the dining chain's competitors were more likely to visit stores such as JCPenney, Kohl's, and TJ Maxx. Building locations near these stores would automatically boost the dining chain's appeal. The alternative data provider extended this analysis to TV viewership habits to validate the appeal of the dining chain's casual menu.
The results validated the approach the dining chain took. Targeted audiences showed a visitation rate 77.9 times greater than the average consumer, indicating the power of correlating seemingly disconnected variables.
Correlating these out-of-left-field behaviors with conventional data, such as user location, informed the dining chain's marketing language and boosted new offer opt-ins.
Alternative but effective
Alternative datasets, like their synthetic cousins, are still gaining ground in the data analytics space. However, they're making a huge impact on firms adopting them. By offering better insights into consumer behavior, companies are designing better products and ensuring high adoption rates in the marketplace.
Ralph Tkatchuk is founder of TK DataSec Consultancy.