Exclusive advice from global influencer and CXO leader, Cyril Coste on how to amp up your customer experience (CX) strategy and connect AI with digital
For years, marketing leaders and brands have treated AI equivalent to an enigma. Some businesses succeeded and some failed to apply AI across marketing and business functions. And why this typically is the scenario is because we often forget that AI is supposed to be the enabler for human efficiency and not an absolute replacement. We must handle it ethically with discernment and human intelligence.
AI can bridge the gap between customer experience, digital experience, and brand experience. Despite all this evidence, why do brands still struggle with AI and customer experience optimization? Let’s walk you through the maze.
Identifying the most expensive customer experience shortfalls
Stellar customer experiences lie at the heart of business success. Nearly 66 percent of customers expect brands to intuitively understand their needs and are willing to pay more for a good experience. It doesn’t end here, a customer-first business is more likely to 60 percent more profitable as compared to businesses that aren’t.
Brands fail to identify and investigate these four pitfalls. Remember, there could be more of these that you uncover with your respective business.
Pitfall #1 – Not using your customer data to understand funnels and customer entry points
This means not collecting the right data and not using it to gain insights on what your customer is doing on your website or your app or your social channels.
You need to use core metrics from Google Analytics to know your customer and understand where they are coming from –
- Are they part of your organic traffic or paid traffic referrals?
- If they came through search engines, what kinds of keywords did they use to reach your brand?
- Where do they live?
- What kind of demographic do they belong to?
- What kind of income bracket could they fall into?
This helps you know your audience and gauge their intent of why they landed on your website/landing page/app, what is their pain point, and how can you improve your customer experience. These are the first steps of designing a digital customer experience that matches the customer’s expectations from your brand. This will also help you map out funnels and drop offs that can be iterated – but if you don’t have the right data sets, you will lack precision.
For example, if you’re a fashion brand, it is complex to select the color and size of a dress while shopping online. If you don’t offer your customers detailed videos or good quality pictures, you will see people walk away from that transaction, and in turn, move away to your competitor brand that may be providing these assets. Your analytics and analysis of different journeys and funnels will help you identify where to focus and how to not lose your customer and revenues.
Pitfall #2 – You win clicks and still lose customers
The key moments when you ask your customer/visitor to make an investment, you need to provide them with context and transparency as to why this is needed. Often, this isn’t clear. This could be a registration sign up or a page which also asks for their credit card details.
It’s very easy for a customer to click but entering information (which is the digital currency) is where you lose customers. Brands don’t communicate the value exchange and fail to match the ask-to-reward connection in such decision-making moments.
There always needs to be an incentive that matches the level of your ask as a business. Let’s say you’re an ecommerce, B2C, or D2C brand, you could collect your target customer’s email and shipping address in exchange for a 15 percent discount reward on their first purchase.
Pitfall #3 – Not conducting ABX testing
Your digital marketers and analysts need to review customer journeys and experiences on their favorite analytics dashboards. Sometimes the drop offs may be for obvious reasons but things get interesting when there might be a set of assumptions that you need to create and verify. This is where you use ABX testing. Start testing that registration page to understand why it failed to get you the desired outcome. The answers you’ll find may be subtle but their impact, massive.
For example, you could use American English rather than British English or add a dropdown of countries to personalize for that particular geography. These tweaks will help you find reasons for drop offs and correlate these numbers with audience characteristics and demographics, thus adding a layer to your customer experience.
Pitfall #4 – Inconsistent information
There’s no magic bullet for frictionless customer experience but consistent information can be the differentiator. If a consumer is on a website or an app the experience doesn’t need to be the same since their entry points and demographics will be different. What is important is consistency in terms of the information you deliver. Use relevant information you have gathered on your customer from different channels and target it on the current website/app they are on with your brand.
For example, Ryanair DAC is a low-cost airline carrier in Europe. Their tickets are cheaper if you purchase them on the app rather than their website. They use the same information, the same data, the same branding on the app. But the pricing strategy changes to help them improve sales and also tap into a younger demographic that is price sensitive.
When your consumer is on a mobile app they are less likely to switch to a comparison. However, on a website, it’s easier to lose a customer if they switch to another tab for price comparisons. To surpass this obstacle Ryanair could display an offer price that is cheaper than the price shown on the website if the customer installs the app. This drives sales and gets the customer closer to the brand and it’s digital ecosystem – which will further refine the customer experience.
The six: countering CX shortfalls across channels with and without AI
1. Focus AI on reducing customer churn
Customer retention is more valuable than customer acquisition. Use AI to detect customers who are likely to leave your brand. Focus on reducing the customer churn by identifying customer behaviors and signals.
Let’s say a customer left your brand after not ordering anything for 12 months. You need to find out what happened before? What are their characteristics? What behavior did they display in their visits and their email interactions? Use these data points and AI to build a model around customer churn intelligence. This helps you identify common patterns if an existing customer reduces their order value by 50 percent in six months, or stops opening your marketing emails, or has unresolved customer complaints logged with your customer service.
AI helps you go beyond the obvious and can also hint towards customer service shortfalls that need focus.
2. Complement AI chatbots with your human customer service support
Proactively I would not recommend AI to interact automatically and directly with your customers. It’s very frustrating that chatbots are usually just used as glorified Google search. Be clear with your customer about what a chatbot can answer for them.
For example, if I want to buy a green jacket but my size is not available, and I go to a chatbot asking, “when will this jacket be available in my size?” The chatbot will almost never be helpful with a specific answer. Brands need to ensure that their chatbot is useful and connected to a wide range of information, not just their FAQ which the customer can go through by themselves.
Work with your customer service experts to identify why people call customer service or email or use the chat service. Build bots that can answer around 80 to 90 percent of these questions. Of course, a chatbot will never be able to answer all the questions, but this removes friction and the lack of customer service efficiency by satisfying most of your customer’s typical queries.
3. Question your assumptions to recalibrate your customer service strategy
If you launch a new product use AI to help review the journeys and understand, how your customers want to get help and create support accordingly. Question your assumptions – does offering email support suit the customer’s real time purchase cycle? If your customer gets an email response in 48 hours that’s not good enough. However, social media can be a faster, better way to deliver prompt customer service. But since social media teams are not connected with customer service teams and their portals the process doesn’t go any faster.
If your chatbot provides next step recommendations, ensure it is relevant and in the timeline that is relevant to the customer expectation. If a customer is on your website, interacting with a chatbot they need an answer now. Where most of the discrepancies and disappointment happens with chatbots comes from reaching a dead end – is when you arrive at the end of the road, you are always pushed to us send us an email or somebody will contact you in the chat orchestra and they see where you are in real time interactions and then suddenly takes like, half an hour, one day two days. So be sure that you match customer expectations. I know it’s very hard because you are at one point, limited by your capabilities, your human capabilities in terms of operations especially at big time.
Know what to automate throughout your business.
4. Secure your omnichannel transactions with AI automation
Help prevent payment fraud and improve error free transactions for your customers. Going back on the business side of omni channel marketing, apply automation in terms of your business operations around accounting and financial reconciliations. This can also be for payment error detections while someone fills in their shipping address.
5. Personalize for the person
Using AI to interact directly with your customer can be a successful exercise only if used for product recommendations resulting in a great sales boost. Use the customer characteristics to target your product recommendations tailored for sub audiences.
For example, if a 40 to 50 year old male living in the UK ordered a green jacket, your AI can recommend a jumper that complements that jacket. Use AI to push product recommendations that are really targeted to an individual rather than a generic audience set.
6. Reverse funnel your customer data into audience research
What I have noticed is often companies use AI to base their assumptions and understanding of how their customers use an app or website. They rarely use AI for customer research.
AI used in your chatbots can also be used to capture keywords and contextual data that can later be applied to push notifications specific to the day, time, weather, customer characteristics, and purchase history. In addition these data inputs coming straight from your existing customers will pack an extra punch to your digital marketing, content, and omni channel marketing strategy as a whole.
AI is going to be just as good as you use it. Think of it as muscle memory that gets stronger with practice. You obviously need to be mindful about how it is applied in the real world in terms of sentiment, context and situations which is where your human element is the key. And definitely, if you are a big brand or a medium-sized business, there are lots of approaches. This goes back to his how AI will improve your marketing strategy in general, and how it will optimize your marketing operations.
There are tons of things you can automate and identifying the critical ones is half the battle won. Then make the right triggers to make customer experiences more intuitive. Understand that meeting certain expectations and not necessarily exceeding it all the time is also a win when it comes to consistency. Because consistency makes your customer feel safe. The pandemic has shown us the value of making your customers feel safe. Stay true to your brand values in terms of knowing how personalized or how much you want to let the consumer know that you know about them.
Invest in your talent in terms of data scientists, data, and eloquent people. And obviously your marketing talent needs to upscale in terms of understanding their data basics to be equipped enough to immediately identify trends and apply them using AI.
Article originally appeared on ClickZ.
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