90 Days to Usable Data

Here’s what’s holding your data-driven marketing back — and here’s what to do about it.

90 Days to Usable Data
Adobe Products Featured

The goal of Pat’s Pantry is simple — give people time to enjoy a healthy meal at home. But, to this fictitious on-demand meal kit service, it’s not about the actual food they deliver. Instead, it’s about the experience — an experience that transcends far beyond the meal itself.

To deliver these memorable, meaningful experiences that exceed their competition, Pat’s doesn’t just need fresh produce and meats — Pat’s needs data. The challenge is clear: while Pat’s has data on everything from who the customer is, to where they live, to what foods they eat, don’t eat, and can’t eat, trying to understand it in a cohesive, usable way has taken the brand down. It’s time consuming, resource heavy, and the pieces never quite come together.

Data counts — and everyone knows it

Pat’s is not alone with its data woes. Big data has never been bigger — literally. Within that data lies your brand’s ability to deliver unparalleled customer experiences at scale — experiences customers want, and experiences that will be your brand’s competitive differentiator now and in the future.

Before you can leverage your data to deliver these experiences, though, you need to ensure its “fit” — usable and ready for action. If it isn’t, no amount of customer information, interactive touchpoints, and third-party insights can help you drive those critical breakthroughs.

“The ability to create experiences that are relevant, personal, and timely, via moments that assist the customer through their journey, is a priority across the entire enterprise,” says John Bates, director of product management for Adobe. “Any person who owns any of those moments needs the right information and context to make them magical.”

Worse, though, having out-of-shape data can threaten your business. In Pat’s case they were collecting a lot of data, but, because it wasn’t pulled together in a cohesive customer profile, it wasn’t as usable or as actionable as it could be — it needed to get in shape fast so Pat’s could start delivering more meaningful experiences to its customers. Without it, their equally data-driven competitors would no doubt pull ahead and start stealing market share.

Your 90-day plan to total data fitness

All of this — the siloed data and the missed opportunities — isn’t who you are, and it’s not what Pat’s Pantry wants to be, either. You want to ensure digital privacy and data security that are completely in step with experience delivery. You want to use your data to create more consistent, cohesive customer experiences. You want to consolidate data from all sources, bridge the silos, and organize information so it’s clean, clear, and actionable.

If your business isn’t “here,” commit to getting your data in shape. With these six expert steps, any enterprise can overcome common hurdles and get data ready for action — all in just 90 days.

Step 1: Take inventory (8 days).

At this point it’s essential to understand the data you have and where it’s coming from — to take inventory of your data and your sources so you can better understand where data and analytics are coming from.

In Pat’s case, data came from a variety of platforms:

  • Online analytics, including customer sign-ups and opt-ins from an ordering app or web interface.
  • In-house tracking of order and delivery history, including location and food quality.
  • Internal management dashboard with intel, such as status reporting and alerting.
  • Product and supply data, such as food and ingredient sourcing.
  • Third-party customer satisfaction data from an outside market research firm.
  • Content management system (CMS) data culled from content creation spanning mobile app and website.
  • Email campaign data.
  • Ad network display campaigns.
  • Social promotions.
  • Customer relationship management (CRM) data for corporate clients and large accounts.
  • Data management platform (DMP) with first-, second-, and third-party data sets.

It’s a lot of data coming from a lot of disparate sources — but it’s not uncommon. Even for a small B2C business, there are tremendous numbers of data points, insights, and observations bubbling up 24/7. And, again, capturing them is just half the battle.

Step 2: Establish expectations and business purpose (20 days).

Now that you know what you have and where it’s coming from, meet with stakeholders and set realistic expectations. Also, look for low-hanging fruit.

“Identify one or two opportunities where you have the greatest opportunity to leverage data to drive action — and then measure the success,” John says. For Pat’s, it’s creating more localized meal options, as well as targeted delivery options for its most engaged customers.

While those quick wins are great, it’s also important to define your higher purpose — essentially, the “so what” behind data efforts.

“That’s going to inspire people throughout your organization and inspire alignment,” John says. “It helps secure buy in from executives so they can push these initiatives from the top down, and gives justification to the long-term effort.”

Step 3: Map what’s missing (20 days).

Once you’ve established your data sources and what you’re collecting, use Launch by Adobe for tag management and ObservePoint — an Adobe technology partner — to perform a tag audit. Here, you want to identify any content-tracking variables collecting inconsistent information. Additionally, tap into common data models to unify transactional, behavioral, financial, and operational data, and move into a single, actionable, and holistic customer view.

During this process you’ll also likely identify areas where data collection falls short. “If data doesn’t have the key — a common data field across data sets — that you need or there isn’t a common key, then take the needed steps to get that data created and collected appropriately,” John says. “It’s never a wasted effort.”

It’s also critical to ensure common keys are created to link AR and VR data, mobile data, and voice-based device data to other intel and insights. Understanding these points will impact how future data is collected and, with it, your workflows and processes going forward.

Here, you’ll also want to review data cleanliness and how complete the information you’re capturing really is. In Pat’s case, they determined the food and ingredient sourcing data has issues and doesn’t integrate easily. With that knowledge, they can de-prioritize this data and work with the vendor to improve future collection.

“The collection, the cleansing, and the processing of data can all be aided with AI,” John says. “Through this process we get rid of about 80 percent of the work a data scientist would have applied before he or she even got to start on the fun stuff.”

Step 4: Prioritize your data needs (12 days).

Get to this stage and you’ll likely have more data sets than you can handle — given Pat’s broad-reaching data sources, they definitely would. “Don’t get bogged down,” John says. “Prioritize your data needs based on how critical they are for the business purposes you established in step two, and record the value each data set provides, relative to the effort required to connect it.”

Pat will also look at the opposite end — the simplest, most complete customer data. In this scenario, it’s customer sign-up and opt-in data pulled from their ordering app (tracked using Adobe Analytics), as well as CRM data.

From there, Pat’s Pantry can integrate data into an experience system of record and create unified customer profiles. Combining data sets not only streamlines and simplifies experience delivery, but also improves data governance.

Step 5: Ensure privacy through targeted data governance (15 days).

With the recent move toward more customer-centric privacy rights, it’s essential for organizations to take time to gut check their workflows and processes. “Once you’ve closed any privacy gaps that exist,” John says, “you’re just about there.”

In reviewing your data collection and usage workflows, ensure you’re in sync with current internal and external regulations and data privacy laws. During this assessment it’s also important to understand new regulations and policies coming to the surface, so your organization can protect itself from future liability.

This future-proofing is built into Adobe Experience Platform, enabling customers to roll with changing privacy standards and environments, while still successfully collecting, managing, and leveraging data.

Step 6: Build a road map and start using your data (15 days).

With goals, inventory, and priorities organized, outlined, and aligned with your business, you — and Pat’s — are ready to flesh out a comprehensive road map of the processes and technologies you need to set the data wheels in motion.

To do this, refer back to the main purposes you established in step one, with an eye on applying data to one business purpose at a time. From there, you’ll be able to keep moving forward, tackling challenges and goals as a data-driven organization.

For Pat’s Pantry, this means collecting reporting requirements from the ordering app and mapping this against available data to determine key questions. For example, Pat’s marketing team wants to know if customers in the Pacific Northwest order different meal kits than customers in Florida so they can, potentially, begin customizing offerings accordingly.

They also want to understand if there are commonalities between their most valuable customers — what they like, what they don’t like, and order frequencies, for example — so they can customize offers and packages.

“Have a path in place, whether that’s to update your technology or your internal processes,” John says. “Then go into action and learn from it.”

Identifying — and accelerating — use cases

Now it’s time for Pat’s Pantry to roll out the full-scale data integration. In Pat’s case, they’ll turn on server-side forwarding between their analytics and DMP for real-time audience enrichment. They’ll also leverage the Analytics and Adobe Target integration for integrated A/B testing and targeting campaigns.

That said, keep in mind that data is only as good as the value it delivers — and the value your stakeholders see in it. “To show the value without question, you need to nail the specific key results you will use to measure success against a business objective,” John says. “Demonstrate the degree to which you can pull together data, and then execute against one use case, rather than trying to boil the whole ocean for stakeholders.”

“After that, optimize. Uncover key areas of your customer experience to improve. Design A/B tests that minimize bias and maximize test efficacy measurement.”

By looking at the data and reporting, Pat’s data analyst noticed that Pacific Northwest customers prefer fish-based meals. Armed with that information, they can create a test campaign to maximize subscriptions for prospects in the Pacific Northwest — testing hero banner imagery with different fish-based meals — salmon versus halibut versus tilapia. They could also easily test food categories like Indian, Coastal Carolina, and comfort. Tapping into customer preferences helps deliver a better meal experience for its customers, while also growing revenue for Pat’s Pantry.

“And, beyond all of this, don’t forget to share insights and evangelize the success across your business,” John says. “Keep the momentum going, and keep everyone on board with your data fitness. Do that and you’ll be able to easily expand to other stakeholders.”

Read more about enterprise organizations putting their data to work in The Data Rush: How to Strike it Rich series and No Email? No Problem. Then get more insights on why data scientists are in demand.

Recommended Articles