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WEBINAR OVERVIEW Delivering exceptional customer experiences with cognitive solutions

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In a recent InternetRetailing webinar, Deliver exceptional customer experiences with cognitive solutions, we heard from Neelam Kharay, customer experience analytics specialist in the IBM Watson marketing team on how retail brands can deliver the deeper and richer customer experiences that they aspire to. Here’s a bulletpoint overview of her presentation.

Neelam started the session by explaining how IBM was working with brands and retailers across Europe to use cognitive technology in their businesses to deliver relevant customer experiences, and what their aim was.

“As retailers we all want our target audience to visit our store, whether they’re online or offline, and when they do we want them to become our customers and buy from us. When they do we want them to come back to us again and again, choosing us over their competitors.

“We know customer experience plays a key part. If our customers have a great experience from that first interaction and throughout their lifetime with us they’ll become vocal advocates, but if they have a poor experience they’re more likely to walk away and go elsewhere.”

• IBM study found two thirds of CMOs reported deeper, richer customer experiences as top marketing priority.

Data: the more we collect the more we’ll know our customers and the better we’ll understand them.

Types of data

Structured data: eg transactional, customer records, predictive

Data outside the firewall: eg news, events, weather, social media

Unstructured data: eg Internet of Things, sensory data, images, video

“As marketers and commerce teams the problem we face is not necessarily the volume of the data but the work it takes for us to understand it.”

How to use the data

Ask questions: what are my customers doing, where are they struggling, where are they in their journey, what are they interested in, was the journey what they expected, what do customers want and what are their intentions?

• Watson Marketing: understand unstructured data from images to tweets, and then reasons by recognising patterns in the data. Creates recommendations and probabilities for each recommendations. Learns continually.

• Powerful analytics, micro and macro view: show what the customer is doing and what they are likely to do next.

• Understanding the journey: identifies patterns that affect the outcome, so the user can understand behaviour across journeys.

• Insights – based in which geographical region, lifestage?

• Cognitive analysis of the journey: marketer can visualize customer journey in relation to different goals, Which journey resulted in more revenue, delivered a repeat visit, greater lifetime value.

Example Google maps: gives a level of insight into journey timings/costs. Applying this to retail, was it the best path for the retailer in terms of cost/opportunity?

• Understanding the relationship: mapping behaviour patterns and struggle analytics predict how customers move along their journey.

• Using interactions, milestones and mindsets to predict how a customer will move through their journey.

Example Starbucks personalisation campaign: aims to address every single person by name – but they get my name wrong often, so I go elsewhere. Imagine if Starbucks were able to assess the impact of that campaign and work out if it’s worth addressing.

• Understanding the opportunity: using IBM Watson Marketing Real Time personalisation, with cognitive rules advisor: can choose whether to accept, amend or reject cognitive recommendations. Better optimised by decision for next time.

Case studies

Ernsting’s family

German fashion and homewares brand. Started with customer experience analytics, building better segments and personas to understand customers and personalise marketing efforts. Now working towards building a single customer view, across email, mobile, online, and the store.

Performance Bicycle

US retailer that emphasises the customer experience. The in-store experience is very personal with experience, advice from bike experts that boosted sales. In the online commerce store there was a very different story. Customers found it difficult to navigate. The retailer looked at where customers were struggling, and analysed where they had high cart abandonment in order to understand frustrations. The data helped to shape plans for a website redesign. Today online customers receive same guidance journey as would receive in store.

In summary

Automated data-driven actions create better customer experiences.

The webinar was followed by a Q&A session. To see the webinar and the Q&A session in full, visit the IBM Watson webinar page.

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