With Cookies going away, and Apple hosting pixels which reveal open rates for emails for Marketing Automation there are concerns – driven by privacy spurred by GDPR and cookie consent – the DMPs are dead. With the latest iOS 15 update the IP addresses which revealed location data also took a nose drive
First party data which brands hold in data warehouses and data lakes are akin to the 11 finger lakes in Upstate NY.
Disparate, with no data governance platforms / frameworks / normalized data or elastic data is still nascent at many brands, with no sign of a Chief Data Officer, albeit staffed with a good sized platoon of data scientists.
The Data ship needs a captain, and the lakes need to be bridged & governed
Fig: The Disparate State Of Data
Few years ago, at the advent of Social listening, the term social selling kicked in and at a Payor a tweet ‘Hello, I am turning 26 on and no longer going to be on my Mom’s insurance’ was a melody.
Just this nugget, a trigger to the Healthcare marketer was exciting. However now with RPA incorporated into social listening intelligence these finds are self-driven.
When I was at Cigna and set up their Social media practice from scratch, the key piece from an intelligence perspective was ‘social listening’. I did set up the Social listening listening along with the publishing and the front line conversations.
Except today, this ’nugget’ would be picked up by AI without any human intervention
Real Time Marketing needs to get Real
Real-time marketing capabilities do emphasize the actionability of marketing capabilities, however the entire chain is not strong enough yet allowing marketers to actively manage their marketing activities and track marketing capability development for products and services improvement, relying on AI -driven insights.
The same Real-time marketing capabilities also help marketers gain situational awareness for their marketing actions, which allow marketers to focus on each customer conversation that matters most to customers with real-time monitoring and big data analytics. By doing so, marketers can make the right decision at the right time
The Buyer’s Journey, The Sales Funnel – allow visitors to evaluate a product & formulate a decisioning rationale to make a purchase and then continue as customers and exhibit possible loyalty. See Fig 1, a couple of paragraphs below.
Marketing Goals have not changed but the goal posts have
The goals for the business and marketers is to generate more conversions (which primarily consists of sales). They deploy various marketing tactics, Marketing automation – segment the population set and send personalized communications via eMail/ SMS etc. They supplement their marketing efforts (MQLs) Marketing qualified leads and then filter it down to (SQLs) for the sales teams to follow up. The brand communication support the effort via various tactics and channels from SEO/SEM/ Targeted Advertising, Social media, Retargeting, Conversion rate optimization, and other methods.
Internal Data clean-up for AI – please can we do this first?
Lately, many brands have been able to clean their internal data, removed and eliminated disparate data warehouses and data lakes, made it possible for the data to be housed and queried. The data science teams have worked hard to make this happen as a result AI has been able to isolate, pick-out customers and gently nudge customers towards marketing and sales cycle, and improving the conversion rates and keeping the cost of acquisition (CAC) within manageable budgets.
Customer Loyalty and Retention reflects the LTV (Long Term-Value
S. Ernest Paul
Fig1: Sales Funnel / Buyers’ Journey / Customers Lifecycle
If the Retention is not holding and the data is elusive customers are fickle they will leave for a competitor, especially now when the COVID tension is high and attention spans are shrinking. We really need ‘Nudge’ to sharpen the experiences and personalize the journeys or we will end up with a Chinese Menu with a million items to choose from – a maze. Now compare that to ta Starbucks menu (Nudge-inspired). Thank you, Prof Thaler @Kellogg for Nudge economics.
Fig2: Customers leaving for a competitor
A New Alliance is being Stitched – CIO & CMO
To stop this leakage and to have the ability to identify, predict and react in near real time a new team/ alliance from within has been born. The key decision makers in defining the Long-Term AI strategy are the CIO and the CMO. Lately the role of the CDO (Chief Data Officer) has taken shape as the origins of all AI driven activity points to the glaciers of data.
Fig3: The stakeholders involved in shaping and championing Data, Machine Learning, AI
The technologies involved before AI kicks-in
S. Ernest Paul
How do artificial intelligence, machine learning, neural networks, and deep learning relate?
Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.
That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.
The nodes at work below are illustrative
If Retention is not sticky and the experience is elusive, the customers are fickle and will leave for a competitor unless of course you are locked into a Xfinity / Comcast deal from which there is no escape for they only tailor to ‘acquisition’ (new customers). I , along with many of us are in the ‘retention’ phase – we get little attention & deals – of course specific to the industry with complete disregard to the sanctity of the ‘Sherman Act’ – my apologies I went on a tangent there).
Data still remains a nemesis
S. Ernest Paul
My creation below illustrates the phases and advances towards AI – however data remains a nemesis. Without it, Marketing and Sales teams are blinded. The Marketers still use Marketing Automation platforms & Lead Generation tools crunch MQLs (Marketing Qualified Leads) with segmentation / identity / personas / personalization – however the ROI on the the MQLs translating to ( SQLs) Sales Qualified Leads is shabby.
AI can only deliver if the data is reliable
Awareness > Consideration > Decision > Loyalty
The No. 1 goal for most businesses is to generate more conversions (which primarily consists of sales). This can be through their marketing efforts, sales tactics, brand communication, conversion rate optimization, and other methods. Of late, many companies have developed critical competencies in using AI to nudge customers towards sales, and have improved their numbers drastically as a result.
The customers are increasing puzzled with the COVID phenomenon and there is mad scramble to get UX / Design thinking and User-Centric design right.
AI, machine learning, and big data technology can all work hand-in-hand to improve the customer experience and support an optimized customer journey, which leads to more conversions in several key ways.
Let’s talk about how you can start using AI tech in each stage of the funnel.
Awareness
Marketing strategies these days are often heavily focused on the top of the funnel to build brand awareness and attract new customers. For many businesses, recognition is nearly equivalent to the value of their brand. Elena Veselinova and Marija Gogova Samonikov explain in their book Building Brand Equity and Consumer Trust Through Radical Transparency Practices that brand impact is a continuous process that insures purchases, cash flow, revenue and share value. Brand communication and experience creates and builds a loyal base of customers that do not consider any other brand.
Brand Awareness
Creating a strong level of brand awareness takes time and strategy. Companies spend millions of dollars on marketing campaigns and advertising to increase their reach and recognition, but AI tech is able to take the guesswork out of these strategies by analyzing huge volumes of consumer data for more targeted campaigns. For example, predictive analytics software can collect, track, and analyze datasets from past customers to determine which strategies or tactics performed well. These datasets are turned into reports with insights to guide marketing efforts and place relevant content in front of the most interested eyes at the right times.
With AI-assisted marketing, advertising strategies can be backed with data to optimize ad placement. Machine learning systems can even identify the best influencers for brands to partner with in order to reach relevant audiences and grow brand familiarity.
Consideration
The next step of the buyer’s journey is often overlooked by marketers because it can drag on for a long time, depending on the product and the customer’s needs. During the consideration phase, a customer is already familiar with a brand or product but are unsure of whether or not to actually purchase. Customers will typically research the product’s reviews, compare prices to competitors, and look for alternatives during this stage. Due to this, the number of potential customers tends to narrow down considerably as they move from this step to the decision phase.
Brands must work to combat each customer’s concerns and questions standing in the way of a purchase decision. One of the best ways to do this is by offering personalized content that is relevant to each person, making it easy for them to find the information they are seeking.
AI systems can be used to predict a customer’s needs based on consumer data and previous online behavior, and then encourage conversions with a tailored UX or even a completely customized landing page that displays content relevant to that customer.
For example, if a site visitor has viewed a certain product page and played a video demonstrating its features, these actions can trigger an AI system to target them with personalized content that prompts a conversion if they don’t proceed to buy immediately. This content could be something as simple as an email message with more information or a display ad with a special offer for the specific product.
Then there are platforms that use conversational AI tech (such as chatbots and voice assistants) to power automated, text- or audio-based interactions between a business and its customers. These platforms can understand speech, decipher intent, differentiate between languages, and mimic human conversations with great accuracy. Increasingly, they are advanced enough to even understand individual context and personalize the conversation accordingly.
Data Insights
Based on data insights, AI tech can curate content that matches up with the issues that are most important to that person, whether it be product features, immediate delivery, long term savings, etc. Customers respond quite well to personalized offers — an Accenture study reported that 91% of consumers are more likely to purchase from a company that sent them targeted deals or recommendations.
Decision Making
Once a customer moves from consideration to action, AI tools can be used to support a positive sales experience and eliminate any bumps along the way. If a customer encounters an issue while browsing the site, or during checkout or payment, it could be an instant sales killer, if it isn’t handled immediately by something like live chat.
A challenging consideration towards a design response is certainly necessary.
According to multiple studies, one of the most frustrating parts about online customer service is long wait times. By using AI-enabled chatbots, companies can instantly answer common questions and resolve issues or roadblocks affecting the progression of the buyer’s journey. And customers certainly appreciate these quick response times. AI systems can significantly increase conversions with effective personalization and swift customer service.
Loyalty & LTV
The last step of the customer journey is possibly the most valuable. Over half of customers reportedly stay loyal to brands that “get them.” Returning customers also tend to spend more money than new ones, and an oft-reported stat says that on average 65% of businesses’ revenue comes from existing customers.
What can the Business do
Businesses (and customers) can benefit greatly from loyalty programs that are backed with machine learning technology. Starbucks famously uses AI tech to analyze customer behavior, improve convenience, and identify which promotions would perform best based on that person’s drink or food preferences, location, and purchase frequency. Their loyalty program uses this data to send out thousands of offers each day for the products their customers are most likely to buy. Their customer loyalty program grew 16% YoY last year as a direct result of their Deep Brew AI engine.
While a positive shopping experience and great products are certainly important factors in a customer’s decision to buy again, data-driven marketing campaigns that encourage loyalty can also help a company to grow their numbers of repeat sales. Again, AI-assisted personalization techniques can boost the chances of a customer coming back for more, especially if they receive targeted offers or shopping suggestions based on previous interactions.
The Wrap
AI is proving to be the tool of the future for marketers. It allows marketing teams to use predictive insights and analytical data to encourage and assist every micro-decision taken by consumers. AI systems not only help customer
Who is S. Ernest Paul ?
Notable – He was recently recognized in the Top 10 CMO in the Country in 2021
b) He was recognized in the top 100 in Finance in 2021
Finance Magazine | C Level Focus
A thought leader in Data, Digital Strategy, Social Media, Content Platforms , Content Strategy, Digital Marketing, Adtech, Access Management, Identity, Adtech, Search engine Optimization, ML, Neural Networks, AI, and a Marketing Technology editor at Digitalbrine.com and author.
He is also a staff writer at Medium for ‘Data Driven Investor’ and other publications.
