How many times did you find yourself using your mobile to find all necessary info and deciding on a certain product while avoiding interacting with a shop assistant?
Can you imagine how delightful your shopping would be if the window display of a shop would recommend you just the right suite while you walk by, based on your searches, upcoming meetings and the business role models you look up to?
During this decade, success for retailers will translate into their ability to bridge the offline and the online experience with original and functional solutions, The Omni-Experience. This will be built around a complete single view of the customer, delivered by a deep fusion of the offline with the online customer behavior data. This will need to be combined with most advance AI technologies in order to provide a delightful, highly personalized & predictive customer experience across any channel at any time for each single customer.
Omnichannel retail is not a novelty anymore; all the way from searching to choosing, buying and picking up, customers complete their purchases across a multitude of channels. It is no longer enough for retailers to have an “omnichannel strategy”.
Today’s global consumers are demanding a unified retail experience, allowing them to move smoothly across the channels with full visibility over inventory, pricing, shopping and checkout experience.
These expectations are generated by the new paradigm of the "digital convenience". At the moment, it creates frustrations for all in the physical retail. And it changed how customers shop inside physical retail and how we value retail properties.
Those retailers that will design and deliver their retail experience based on the new "digital convenience" paradigm will create a delightful experience for their physical customers that will allow them to survive and thrive in the next decades.
Steve Dennis expresses this as the "Remarkable Retail". Shopping is inherently more experiential and that’s what the physical retail is really all about. Customers' behavior is dynamic and it is an expression of convenience in relation to their searches and shopping needs and expectations. That`s why one cannot divide these two anymore. Digital drives physical. Physical drives digital.
New technologies allow retailers to map the entire customer experience across all channels and provide timely insights into their behavior. Furthermore, this means also the capability to use behavioral data to build very rich profiles for individual customers or for aggregated audiences, more intimate, valuable and relevant than ever before.
These new technologies can include:
- Offline customer behavior analysis
This means that each individual indoor visit and the multitude of touchpoints are all used to build a model to understand who customers are and what are their needs and expectations. In order to achieve this, new technologies can use video cameras, Wi-Fi, thermal cameras, floor sensors, Bluetooth Low Energy, NFC, GPS and other sensing and localization capabilities. This results into a deep understanding of individual shopping paths and the interactions of these paths as they cross multiple product categories and store areas.
- Online customer behavior analysis
This means that each individual user session on an website or mobile app is logged and tracked from their source (like a certain display ad, or a search engine, or any other scenario) throughout all their actions with the screens, content, products and buttons across their journey until exit. Technologies like cookies, browser identifiers, server-side logs and other digital tracking technologies can understand unique online paths within an online retail marketplace or individual ecommerce destinations and how users personally engage with categories and products.
- Probabilistic profiling models
These are advanced Machine Learning (AI) algorithmic models where raw behavioral data can generate very rich and marketing relevant data like socio-demographics and psychographics (like values, desires, goals, interests, and lifestyle choices). People's behavior reflect who they are, this is why it makes sense for the shopping basket on an ecommerce website to tell us who that shopper is (gender, age, life style, life stage, preferences, habits etc.) without the user explicitly sending this data to us.
- Lookalike models
These are advanced Machine Learning (AI) algorithmic models where certain users in our database (Seed Audience) and all their profile data are being used to discover other users (Target Audience) with very similar behavior or characteristics. This is primarily used to expand audiences in marketing and to discover new valuable customers. But it can be also used to do user data enrichment: if there is some data missing from our Seed Audience (let's say the average monthly household income) and this data can be found in our Target Audience, it makes sense to use this information in our Seed Audience as well.
- Predictive models
These are advanced Machine Learning (AI) algorithmic models where future behaviors are predicted with a certain confidence based on past behavior and patterns, including outside factors (like weather, holidays, day of the month etc.).
- Recommendation models
Here we talk about advanced Machine Learning (AI) algorithmic models where the system suggests specific products to specific customers based on a high probability score for those customers to have an interest to purchase and use those products.
- Data Fusion
If some of the technologies from above can be found on several platforms there is this one technology to be uniquely found in Footprints that is capable to take all behavioral data from offline and match it to all the behavioral data from online, thus delivering on the great promise of the offline-to-online single customer view. This is one of the most Advanced Machine Learning models capable to do data fusion without a new identifier (like an email address or phone number), but purely based on a very large set of behavioral data for each individual customer.
The extraordinary capabilities of these unique technologies are the key differentiators for the retail of the future to deliver what today we would perceive as the ultimate customer experience. But as market dictates it will soon represent the minimum, mandatory level of customer experience required from al retailers surviving the great retail paradigm shift of this decade.
It is time to move the needle and, as a retailer, to start looking outside transactions and footfall traffic. The right perspective of the future of retail is outside CRM. Your as a retailer need to start putting more focus on more money into Customer Experience Management across all channels and each individual customer: the Omni-Experience.
You can start leveraging the most advanced technologies with more robust Customer Experience mapping models (like McKinsey's Customer Decision Journey model which is my all time favorite) to transform themselves into Omni-Experience Retailers.
In the short terms, this means +50% more Return on Marketing Investments (ROMI). Why? Because when you look into the omnichannel customer experience, you identify pain points and non-sense frictions that stop you from converting more and, as a result, increase your Customer Acquisition Costs.
On the long run, shopping experience as delivered for each customer has great emotional key take-aways that will generate increased frequency of visits, increased visit duration, growth on spending and share of wallet and more organic recommendations. This can add up to +80% better Customer Acquisition Costs.
The most important thing you can start with is measuring the customer experience across all channels. While the purpose of measuring your customer experience efforts is to:
- Track progress on actions taken to improve the omni-experience\
- Identify improvement areas
- Calculate the ROI of customer experience
- Prioritize your actions and invest in the right things
There is more to it. Footprints' capabilities to help you measure the customer experience across all channels cover all the following key metrics:
- Net Promoter Score (NPS) – this metric gives you a snapshot of overall customer advocacy. It measures the likelihood for a customer to recommend your brand experience.
- Customer Retention Velocity Score - this metric shows how fast you’re moving into retaining your first time customers and turning them into loyal customers. It looks at how quickly customers are moving through your onboarding and how much value (purchases, searches, frequency and duration of visits) these customers generate in your overall ecosystem and how fast (i.e. the acceleration of it).
- Customer Effort Score – this metric can help you understand the basic functionality of your offering and its relevance to your customers’ needs. This metric focuses on the ease with which a customer can complete any given task.
- Customer Satisfaction Score – this metric helps you understand how satisfied your customers are with your company’s products and/or services. When you collect this data at various touch points, you can start to identify key drivers of positive or negative experiences at different points in the customer journey.