technology to support personal commerce


Retail Regeneration: Investing in Technology to Support Personal Commerce

By Sherene Hilal

We’re now a year into the shift from store-first to digital-first buying, and one thing has become startlingly clear: A customer experience gap still exists.

In early 2020 when the pandemic hit and the shift to digital was forced to happen overnight, this gap was understandable, despite the consistent growth of time spent in digital since 2010. But a year-plus later, it’s something consumers won’t put up with.

This gap exists because many brands continue to invest in mass marketing strategies. But that approach doesn’t translate from IRL to URL. In the digital world, brands need to invest in real-time personalization, as creating 1:1 experiences that are as dynamic as each shopper is the only way to meet consumer demands.

So what type of technology do you need to offer real-time personalized experiences? And what does it take to get there from the tech stack your brand likely uses today?

How We Got Here: The Traditional Tech Stack for Mass Marketing Supports Store-First Retail

Let’s take a look at the traditional tech stack brands use today. This stack is typically composed of systems built for mass marketing and transactional commerce that were designed decades ago for the store-first world. It usually has four layers: Data management, campaign management, audience management and execution.

traditional tech stack technology support personal commerce

Launching a single, generic campaign using all of these systems takes 4-6 weeks and requires significant manual work. It also offers little-to-no optimization based on performance from previous campaigns or past customer engagement. That might have been okay in a store-first world, but that lack of speed and individualization doesn’t cut it in the digital world. 

If your brand has to wait over a month to get a campaign out the door, your shoppers will have already moved on and bought elsewhere. And if you’re sending one-size-fits-all recommendations to boot, any audience you do have left is likely to be turned off by the generic experience — which you can guarantee will stand in stark contrast to the extreme personalization they’re experiencing from the likes of Netflix, Spotify and (gasp!) your competition, all of which are just a click away.

Where We Need to Go: The Modern Tech Stack for Real-Time Personalization Supports Digital-First Retail

Now is the time to evolve this stack to build a new one specifically designed for a digital-first world. How do you do that? 

To start, you need to unify audience and campaign management in the same system using real-time data on shopper behavior and the product catalog. This means no manual feeds, no list cutting, no hard-coding and no integrations between that coveted “single source of customer data” and how you build campaigns. This unification also means campaigns will always be up-to-date with the latest preferences of your shoppers, all while eliminating weeks of work — we’re talking an average of 70 days of manual and repetitive labor.

unified audience and campaign management technology support personal commerce

Great, so now you’ve saved a bunch of time and have a dynamic system. But what do you put in each campaign? How can you possibly personalize for every shopper? This is where AI meets the real world

That unified audience and campaign management system needs to have predictive intelligence at its core. This is how, with a single campaign, you can have unique content, offers and recommendations for every shopper. It’s also how you eliminate manual optimization, since AI is designed for the feedback loops that improve each campaign automatically. Just imagine never needing to update a single daily send again.

predictive intelligence core technology support personal commerce

Finally, it’s time to execute and orchestrate. Similar to the unification of audience and campaign management, the channel execution also needs to be in the same system. That’s because real-time predictions need real-time channel execution, otherwise you’ll end up manually porting powerful data into systems built as dumb pipes — which will result in irrelevant communications that lose shoppers’ attention.

channel execution technology support personal commerce

A Unified Platform for Personal Commerce is the Future of Retail

It’s now abundantly clear that systems built for store data and mass marketing cannot possibly create the superior experiences required to capture shoppers’ attention in a highly competitive, digital-first world.

What your brand needs instead is a single platform that unifies shopper and product data and uses AI/ML to activate personalized multi-channel customer experiences at the speed and scale of digital.

Consumers now expect a personal commerce experience. They will seek out brands that deliver authentic, always-on experiences that meet them whenever they want, wherever they are — and the only way to do that is to invest in this type of digital-first technology. Critically, making these investments to support a personal commerce experience is vital to thrive in the current state of retail and beyond, no matter what type of brand you are — traditional brand, digital native or department store. 

Interested in learning more about what it takes to make the shift from transactional to personal commerce and how leading brands like Ascena Retail Group are already seeing success with this approach? Click here to watch our broadcast, Beyond Transactional Commerce: Driving Profitability in a Digitally-Dominant World.

Sherene (2) (Copy)

Sherene Hilal

Sherene Hilal is the VP of Product Marketing and Operations at Bluecore. In this role, Sherene leads the commercialization of products and business operations, with a focus on enterprise readiness. Previously, Sherene was the VP of Product Marketing at Curalate, a content intelligence platform that makes images shoppable. Prior to Curalate, she served as the Senior Director of Outbound Product Management for BlueKai (and later Oracle, following the company’s acquisition), where she defined and developed the “Data as a Service” category. Sherene holds a Master’s Degree in Applied Math and Systems from Columbia University and a B.S. in Applied Math from Cornell University.

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