Why your data product needs a good elevator pitch

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In recent years, a term started appearing across the technology world: “data monetization,” turn your data into dollars.. (as we mentioned in a previous, post, you can Find Gold in Your Data!) Businesses reacted to the hype, started spending on every solution under the sun and then… Nothing. Nada. Zilch. In many cases the revenues never materialized, buyers became frustrated with the lack of results and blamed the whole concept of data monetization. The problem is, you’ve got to avoid certain mistakes... and they’re silent killers.

In truth, data products are a great opportunity for most businesses to engage customers and create new streams of revenue. Untapped, dormant data can, when refined properly, become a crucial resource for your company. Fortunately, we’ve worked on many analytics projects ourselves, have seen these mistakes made and have put together a guide to help you avoid making them yourself.

To provide some quick insight, we thought we’d share one of the tips we’ve found most helpful when starting to create an analytics product.

Creating an elevator pitch

Find Gold in Your Data

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"Data Monetization" is a term you might have heard a lot lately.  But what does it really mean for you and your business?  There is gold in your data, but how can you extract it to gain all its benefits without adding resource burdens on your business?  We collected the main approaches successful companies are using to give you inspiration and insight into how you can use data you already have to improve efficiencies, create new revenue streams or increase value and hence your wallet share from your current customer base. 

Use data to make better decisions

It is not always about the big, earth shaking decisions. What if we can empower our employees to choose better paths in incremental fashion? Which ad to place in an available space? How to utilize remaining capacity on a shipment? Those items may each mean just $50.00, or $1,000.00. But people can be easily making 50 decisions like that per day.

Using a Data Prep Platform: The Key to Analytic Product Agility

                                                     

                                                                                      Guest post by Kevin Smith

For a product owner, one of the biggest fears is that the product you're about to launch won't get the necessary adoption to achieve success. This might happen for a variety of reasons— two of the most common are a lack of fit to the customers' needs and confusing design (it's just too hard to use!).

To combat the possibility of failure, many product owners have adopted the "agile" approach to building products that have enough functionality to meet to minimum needs, but are still lean enough to facilitate easy change.

As a data product builder — someone building customer-facing analytics that will be part of a product — the needs are no different but achieving agility can be a real challenge. Sure, every analytics platform provider you might consider claims that they can connect to any data, anywhere, but this leaves a lot of wiggle room. Can you really connect to anything? How easy is it? How hard is it to change later? What about [insert new technology on the horizon here] that I just heard about? If you want to build an agile data product, you've got a tough road ahead... as I found out.

Recently I started working on the analytics strategy for a small start-up firm focused on providing services to large enterprises. As they delivered their services, they wanted to show the results in an analytics dashboard instead of the traditional PowerPoint presentation. It would be more timely, easier to deliver, and could be an on-going source of revenue after an engagement was completed. As I spoke with the team, a few goals surfaced:

  1. They wanted to buy an analytics platform rather than build from scratch. The team realized that they would be better off developing the methodology that would differentiate them from the competition instead of creating the deep functionality already provided by most analytics platforms.
  2. The system had to be cost-effective both to set-up and to operate. As a start-up, there simply wasn't the cashflow available for costly analytics platforms that required extensive professional services to get started. The product had to be flexible and "configurable" by non-Engineers. With little to no budget for an Engineering staff, the team wanted a BI platform that could be configured easily as customer needs changed.
  3. Up and running quickly. This company had customers ready to go and needed a solution quickly. It would be essential to get a solution in front of the customers NOW, rather than try to migrate them to a new way of operating once the dashboards were ready. Changes would certainly be needed post-launch, but this was accepted as part of the product strategy.

None of this seemed to be impossible. I've worked on many data products with similar goals and constraints. Product teams always want to have a platform that's cost-effective, doesn't strain the technical capabilities of the organization, is flexible, and is launched sooner rather than later. It was only after a few more conversations that the problem arose: uncertain data sources.

Most data-driven products work like this: you've got a workflow application such as a help desk application or an ordering system that generates data into a database that you control. You know what data is flowing out of the workflow application and therefore, you understand the data that is available for your analytics. You connect to your database, transform the data into an analytics-ready state, then display the information in analytics on a dashboard. The situation here was different. As a services company, this business had to operate in a technology environment dictated by the customer. Some customers might use Salesforce, some might use Sugar CRM. Still others might use Zoho or one of the myriad other CRM platforms available. Although the team would structure the dashboards and analytics based on their best practices and unique methodology, the data driving the analytics product would differ greatly from customer to customer.

Keboola: Data Monetization Series Pt. 2

             

As we examined in part 1 of our Data Monetization blog series, the first step to increasing revenue with data is identifying who the analytics will be surfaced to, what their top priorities are, what questions we need to ask and which data sources we need to include.  For this blog, let’s take a look at what tools we will need to bring it all together.  

With our initial example of a VP of Sales dashboard, fortunately the secondary data sources (NetProspex, Marketo and HubSpot Signals) all integrate fairly seamlessly with the Salesforce CRM.  This should allow for some fairly straightforward analytics built on top of all the data we’ve aggregated.  If we pivot over to our CMO dashboard, things get a bit murkier.

Although our Marketo instance  easily integrates with Salesforce, the sheer volume of data sources that can provide insight to our marketing activity makes this project a much more daunting ask.  What about our social channels, Adobe Omniture, Google Ads, LinkedIn Ads, Facebook Ads, SEO as well as various spreadsheets.  In more and more instances, especially for a team managing multiple brands / channels, this number can easily shoot into the dozens.

Keboola: Data Monetization Series Pt. 1


When a company thinks about monetizing data, the things that come to mind are increasing revenue, identifying operational inefficiencies or creating a new revenue stream.  It’s important to keep in mind that these are the results of an effective strategy but can't be the only goal of the project.  In this blog series, we will exam these avenues with a focus on the added value that ultimately leads to monetization.  For this blog, lets look at it from the perspective of creating executive level dashboards at a B2B software company.

Who will be consuming the data and what do they care about?

Before we jump into the data itself, take a step back and understand who the analytics will be surfaced to and what their challenges are.  Make profiles with their top priorities, pain points and the questions they will be asking.  One way to get started is to make a persona priority matrix listing the top three to five challenges for each (ex. below.)

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Once the matrix is laid out, you can begin mapping specific questions to each priority.  What answers might help a VP of Sales increase the effectiveness of the sales team and ultimately revenue?

  • What do our highest velocity deals look like (vertical, company size, who’s involved)?

  • What do our largest deals look like?

  • Where do our deals typically get stuck in the sales process?

  • What activities and actions are our best reps performing?