- Build a 360 degree view of the customer
- A Salesforce study shows that the current biggest challenge for marketeers is real-time customer engagement
- It is important to avoid the common pitfalls – learn from the mistakes of others.
- Recent studies confirm that organisations that invested in real time AI see a major uplift in revenue .
Building A 360 Degree View Of The Customer
“Know thy customer” should be the first commandment of every business. Customers aren’t all one and the same, they’re one of a kind. Organisations have been increasingly evolving into customer centric entities to live up to this commandment. They have all been focusing on understanding the conversion path, predicting customer behaviours and then crafting the perfect customer experience across all the customer touchpoint. A truly omichannel effort that engages the customer and aims to build a relationship with him/her on every channel – being it phone, email, app, social media or physically at a store.
Nowadays, customers have great expectation. They expect a seamless experience and immediate personalised attention. They expect businesses to listen to them. Till a few years ago, a response time of a few days was acceptable. Not anymore. We have entered the “age of now” where everything has to happen in real-time. Today’s consumer expects hyper personalized offers, timely recommendations and relevant content. Businesses are expected to deliver a rich customer experience that scales in real-time throughout the entire customer journey.
To help us come with this real-time reality we have the luxury of collecting and storing large amount of data of what is happening in and around our businesses. Yet most businesses still have not made the jump to use AI efficiently and effectively to enhance the customer experience and to be able to go the extra mile.
What are “Customer Experience” (CX) and “Customer Engagement”? Before we delve deeper – let’s take a look at what do we mean by Customer Experience and Customer Engagement. CX is not about the moment the customer buys your product or service. It begins the moment the prospective customer sees your brand name for the first time – and it continues through the customer journey. Customer Engagement is much more than Customer Service. It is ongoing. It is about fostering all those connections that you have in between customer visits. When you know your customer you work at creating that emotional connection between the customer and your brand, a connection that leads the customer to be loyal to your brand. Loyal customers not only generate business themselves but more importantly refer new business.
The above is a Customer Engagement Model from cxLoyalty’s, depicting the drivers to customer engagement.
Building a complete 360 degree view of the customer : before you can take action to enhance the customer experience you first need a 360 degree view of the customer. It is very common to have data sitting in silos all over the organisation – from sales, to marketing, to product, to customer support. These silos need end. You need to build a data warehouse – where all the relevant customer data is in one place – a complete “single version of the truth”. Then you need to share information across business units – so that everyone, and every system works towards a consistent customer experience.
Engaging Customers In Real Time
Collecting, storing, and analysing customer data in real-time is not a simple task. For many data sources data may not all be available in real time.
According to a recent study by SAS (sas.com), 25% of businesses in the UK are adjusting their marketing communications daily based on customer data they have collected while only 16% of UK businesses adjust their marketing communication in real-time based on customer behavior and data collection.
The State of Marketing Report for 2020 by Salesforce shows that engaging customers in real time is the number one challenge for marketers. Nearly 7,000 marketers from around the world took part in this survey, with 84% of them reporting using it some sort of AI, up from 29% in 2018.
17 Examples Of AI In CX
Here are 17 practical examples of using AI to enhance customer experience.
Some examples target directly towards the customer. Other examples help the staff/employees to provide a better customer experience.
- Behavioural Segmentation : splitting customers into clusters and sending personalised messages to each segment. Identifying customers that make you money, and those that cost you money.
- Recommendation Engine : Predicting the next product/service the customer is gong to buy and creating an experience around that
- Lead Prioritisation : identifying (predicting) your top prospective customers and focusing on closing them before any other lead
- Churn Prediction : Predicting when a customer is about to leave – the “customer lifetime” – and sending him personalised communication that increases his/her lifetime. One you have the lifetime of the client, you can calculate a more important KPI – the customer lifetime value (LTV).
- Unearthing Valuable Insights And Trigerring Real-Time Alerts : Identifying the abnormal behaviour, patterns, trends and correlations in the customer data that are not identifyable to a human – and reacting to it immediately. (AI models are very good at recognising abnormal patterns). Fraud management is a great example of this.
- Time Series Forecasting / Resource Planning : to allocate resources and budgets according to future predictions by predicting the amount of resources your will need in the hours, days, weeks, months, years to come
- Chatbots through Natural Language Processing : AI & NLP enable chatbots with the right technology and data knowledge to respond accurately and consistently.
- Speech Recognition & speech-to-text for incoming calls including calls in multiple languages.
- Visualistion / Dashboarding : proving the c-suite and other key decision makers with the actionable insights in well presented dashboards
- Sentiment Analysis : determining the mood of your customers by text mining the customer support tickets, customer feedback, reviews and the twitter/facebook feed.
- Timing : predict the best time to reach your customers for each channel.
- Simulation : running simulation of what could happen if a certain variable/scenario changes.
- Hyper-Personalised Promotions via detailed customer personas/profiles – the most popular of which are automated emails that match the exact customer profile.
- Self-service Support : studies show that customers are increasingly preferring self-service done right – through fast, frictionless and personalisation, rather than traditional support channels like phone calls.
- Support Your Customer-Facing Employees : giving the AI enhanced tools to customer facing staff to impress the customer
- IOT devices – intelligence devices at the edge (where the “action” is happening” and in stores – collecting data and taking decision on the spot
- Campaign automation & Automated A/B testing : using AI to take decisions that can put your marketing tools on auto-pilot
Measuring Customer Engagement : For each example above a key step to success is to measure the levels of customer engagement : you cannot improve what you cannot measure. The simplest best KPI is the Net Promoter Score, however you can also look at other KPIs like the time the business is taking to respond in a personliased way and/or the time time it takes to actually solve the issue once AI is injected in the process.
Challenges & Pitfalls With Real-Time Machine Learning Models
Using AI to enhance the customer experience brings both incredible opportunities and hefty challenges. While a hyper-personalised customer experience might sound like the holy grail of marketing it does come with its own set of challenges and achieving ROI from investing in real-time AI assigned customer profiling requires you to know and to tackle these challenges.
Take a look at the Team Data Science Process (TDSP) – an agile, iterative data science methodology.
A set of challenges crop up as soon as you decide to run with such a methodology in real time.
1. Continuous Retraining Of Machine Learning Models
Retraining Machine Learning models can be a challenge on its own. Take everything to “real time” and the challenge is exponentially bigger. The following few steps – that are used in adopting AI models, need to be repeated as often as possible to keep to the real-time requirement.
- Getting data scientists to select/create/maintain the machine learning models.
- Scaling up and collecting data from as many data points as possible across the entire customer journey.
- Cleaning of the data and preparing it for analysis.
- Doing real-time analysis of a large amount of data.
- Training machine learning models to precisely profile each customer.
- Feeding the results from the machine learning models into the marketing and other key systems.
- Retraining the machine learning models with the latest data. Repeating steps 2 to 6.
2. Bringing IT & Marketing To Work In Harmony : The biggest challenge for AI to enhance the customer experience might not be technology related. But the way IT teams and marketing teams co-ordinate and share data.
3. Too Many Data Sources : we are drowing in data and it is not that simple to bring it all together. In the State of Marketing Report for 2020 marketers say they have a median of 12 data sources to deal with.
4. AI Is Not The Holy Grail : It is sometimes a challenge to convince decision makers not to think of AI as the “solve it all”. AI itself is not going to craft a great customer experience. It is not going to refine or scale to great customer experience. AI is an enabler.
5. Hidden Bias : AI models can build biased outputs. It is usually not simple to eliminate this bias hidden in the AI models that are driving your customer engagement.
Investing In Real-Time AI Pays Off
Despite the challenges above and the investment required, recent studies confirm that organisations that invested in real time AI see a major uplift in revenue. By reacting in real-time you can meet customer demands much better – taking the customer experience to the next level.
Delivering a superior customer experience by achieving relevancy at every touchpoint based on an understanding of each individual customer pays off.
Do you want to learn more on how to you can apply AI, machine learning and data science to your business? Get in touch for a free consultation. We will guide you step by step, to reap the benefits while avoiding the pitfalls.