Using AI to fuel experience innovation

With ongoing, rapid change now the norm across all industries, leading companies are finding it critical to continuously innovate on the customer experience (CX) they deliver. These companies not only see innovation as a way to enhance the experience’s effectiveness and relevance across digital touchpoints. But they see it as an opportunity to experiment and discover creative and strategic solutions to problems that reimagine experience altogether. Beyond mere CX, they’re looking holistically at becoming a business of experience (BX).

By using data and artificial intelligence (AI) to capture new insights that realign operations and processes, BX leaders build bridges between their organizational intent and what the customer eventually experiences. They also ensure C-suite buy-in, create a culture of experimentation, frequently test hypotheses, and optimize tactics and ways of working that align these efforts to their primary business objectives. According to Accenture research, 80% of BX leaders said they were very confident about their ability to link their experience innovations to actual business results (e.g., boosts in sales or contract renewals), versus 52% of all other companies.

Before we can get to making experience innovation an everyday habit, it’s highly important for organizations to address their data. The ability to collect, join and standardize a wide variety of messy data from many sources is vital to generating deep insights on customers and helping them build a 360-degree view of who they are and how they purchase.

With that type of data foundation in place, businesses can develop an experimentation capability to test which combination and sequencing of interactions – and at which customer touchpoints – can generate the most value. This means moving from a single use case mentality to an omni-channel one. For example, it’s about knowing how to sequence promotions with brand interactions and determining exactly the right time and assembly of content and creative (i.e., with Dynamic Creative Optimization) to show customers across paid, owned and earned channels. It’s also about knowing whether to engage people who are using a competitive product or target existing customers who show a high propensity to buy new products and services that will increase overall customer lifetime value (CLTV).

Quantifying the value of all these different touchpoints and actions has historically been very difficult to do. The human brain—and even traditional analytics models—can't analyze hundreds of different touchpoints happening across millions of people. There are simply too many interconnected data points and potential combinations. That’s where capabilities like Accenture’s proprietary Attribution Platform come in. The platform’s sophisticated machine learning (ML) models consider all possible touchpoints and interventions and run various statistical algorithms to identify which ones can generate the greatest incremental value. This modeling can be applied to net new users, prospects, cross-selling or upselling of existing customers—or any combination thereof that drives growth. Additionally, because the Attribution Platform uses ML to continuously learn and train performance measurement, it doesn’t rely on third-party cookies like many other performance measurement and multi-touch attribution (MTA) tooling.

Companies can take these insights and plug them back into the experimentation engine to figure out where there’s even more value. For example, what happens if we tweak the messaging or presentation of a product, present a different type of offer, or change the order of interventions in a customer’s or prospect’s path? What if we tried a combination of these tactics with multiple execution partners and technologies?

Experimentation can feel like a gamble. But with the ability to measure performance in a more real-time capacity – as opposed to relying on historical data with weekly or even monthly lag – we can truly create new value from optimizing past customer decisions and experiences. Accenture builds such experimentation engines with our ecosystem partners, which help us put these experiments and tests in the market quickly and at scale. We work with these partners to navigate each of our different client’s industries and develop solutions within our partners’ stacks, fueling experience innovation even further.

Take one confectionary manufacturer we worked with. They had many different customer touch points and disjoined data that prevented them from even analyzing, let alone optimizing, experiences. Our first order of business was to make sure all siloed data sets were connected through our Consumer 360 Platform, from onsite engagements to in-store purchase history. Then we needed to determine the best way to help grow revenue and develop customer loyalty. This led to questions, hypotheses and experiments around how we can get consumers to switch brands or how to price chocolates strategically around certain holidays when demand is high. By monitoring metrics such as frequency of purchase and interactions on site to quantify the ROI of each channel, we used those insights to experiment on marketing tactics that led to higher engagements and improved sales.