Which Data Company Are You Again?

tonyblankemeyer"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Tony Blankemeyer, startup liaison at 84.51°.

I’ve seen startup pitches for everything: new takes on food, digitized shelves, fancy carts, drones for warehousing, robotic chefs, social media management, VR shops, payment solutions, mar tech stacks, ad tech platforms and other types of data companies.

There’s a funny thing about early-stage data startups. When talking about themselves, they all sound the same. Their websites are the worst. Marketing speak and buzzwords are used interchangeably. While it may be important to get a company lumped in with other programmatic or machine-learning companies, the unfortunate trend seems to be to stop the explanation there.

Some of this stems from not wanting to share too much information to protect their intellectual property, but in being so general, these upstart companies are doing a disservice to their product teams and leaving their prospective customers with a lot of room for interpretation.

Same goes the other way: Dropping a laundry list of features and functions may shed light on some of the capabilities of the solution, but without a focus on what makes the tool unique, potential customers may struggle grasping its importance. Introduction and overview meetings can often start with more of the same. After some digging, we eventually arrive at the heart of what some of these companies do. But why does this have to be so difficult?

When meeting with large companies, startups need to be clear about what the meeting is about. Oftentimes, failed meetings are doomed before they start because the objectives are not outlined. Is the startup fully baked enough to share what it believes it can do for the prospect or is the startup at an earlier stage than that – seeking input to help inform the solution being built?

Talking with customers to gather feedback should be a regular part of the product management process, but the amount of opportunities with the right audience in larger organizations is limited so startups must ensure both sides are aligned to the topics of discussion.

Contextualizing a data solution to customer needs is also critical. The startup may have a great extract, transform and load process or a data set with unique attributes of users with associated email addresses, but if the tool’s application isn’t properly described, its value may go unrealized. In which primary use cases will the solution shine? Does it save time or money? Does it drive sales? Will it impact the customer experience and create a stronger connection for future purchases and loyalty?

Helping prospective clients see and feel the trade-off of not using a startup’s tool has been one of the most effective ways I’ve seen startups win new business. This requires research on how the company is set up and homework about who the startup is talking to. Emphasizing the results that may be provided by a solution will help focus the conversation around the metrics that matter most to the customer.

Companies have bought into the value data can bring to improve decision-making. As the industry evolves so will the variety of solutions to help them capture, interpret and action their data assets. As in any developing industry, the market will need to get up to speed on understanding new concepts and methodologies.

Through this education process, bring clarity to your future customers by outlining not only the product, but also the solution to a particular problem. Most importantly, don’t lose sight of what differentiates your startup from everyone else; elevate it.

Follow Tony Blankemeyer (@tblanx), 84.51° (@8451group) and AdExchanger (@adexchanger).

 

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