Tell us what you want, so we can build it.

As a design strategist on the Customer Experience (CX) team, we identified product opportunities throughout the shopping experience.

We consistently looked at all aspects of the Customer Experience. We regularly conducted research and conceptual testing for upcoming and proposed features to the website, app and call center software with customers to see what might resonate with them.

These included simplifying buy flows and building entry points that made the shopping experience more straightforward.

How might we provide customers a more direct path to browse and purchase home internet and mobile phone plans and devices in the shopping experience.

The AT&T digital shopping experience put too much on customers to find their way to what they wanted. Sometimes the navigation choices or search results matched their shopping intent — but many customers became lost or frustrated not being able to accomplish what they wanted.

Talking to customers we consistently were told the site was "too broad" and requiring "too many clicks to get where I want to go."

Most mentioned was how the site forced a device-first path, even though half the visitors came to pick a plan.

Pricing was based on the customers’s credit history or eligibility for discounts. Final pricing wasn’t available until the checkout stage. This is the data that AT&T mostly had already - it was just a matter of identifying the customers and ensuring that any information collected would be used strictly for this discrete shopping experience.

Capture intent → Match data → Generate offers

Combine a customer's stated intent with the data about the customer. Build an experience that directs customers to their stated products and services with accurate pricing and eligible discounts.

Stated intent (quick quiz) + 2) first-party data (credit tier, discounts). Then show how the rules engine built a one-page offer.

Fuse customer intent with customer profile data to build an experience that would direct them to the products and services they came shopping for while using our customer data to present accurate pricing and eligible discounts.

Instead of the customer figuring out where they needed to go based on wayfinding and navigation, they could tell us what they wanted. This was done by asking them generally services they were shopping for (mobile, home internet, TV, homophone, accessories) and then how many lines they might need (for mobile) and how much speed they might for their home Internet.

Then we would ask them for some identifying personal information such as their mobile number so we could use their data to make the pricing and offers more personalized.

Usability tests confirmed customers will share a phone number when the value is immediate and usage is transparent.

This also was information we would ask in checkout anyway, so it was more of a matter of bringing this request sooner in the process to improve the overall customer experience. It also allowed us to streamline the checkout experience since they were less information to collect later.

Based on the customer intent and data, we built a generative web page based on the preferences. This is where they could come and see services and pricing based on their intent.

Early testing showed customers like the streamlined nature and “train tracks” feel of making their shopping experience more catered to their actual intent.

We saw an uptick in people who actually ended up buying services of nearly 1% (about 800 conversions per day) small but significant when figuring the thousands of transactions per day.