Consumers leave behind huge exhaust of data and smart marketers are leveraging it to build superior and relevant customer experience. Now and in future data equity will trump brand equity in a marketer’s arsenal. This needs companies to develop a new metric called Return on Customer Equity. The coming decade will be the age of the Marketing Math Factory. To win, companies will have to adopt marketing math and build creative common sense on top of it. One of the most significant changes the algorithm approach brings for both businesses and consumers is a vibrant new level of interactivity. Algorithms and math will allow marketers to make meaning out of this data and make it actionable.
The TV series, Mad Men created a fictionalized encounter when an IBM 360 mainframe computer physically displaced the creative department of an advertising agency. While that may never happen, but now the mad advertising men and women are running a Creative Math Factory.
THE SEGMENT OF ONE
Major Paradigm Shift the marketer needs to make is to think ‘one to one’ versus thinking mass. Unlike fifteen to twenty years before, the marketing today has become a complex endeavor which involves addressing many customer touchpoints – including email, mobile, online, video and social.
Today, technology enables the marketer to run tremendously personalized campaigns which allow marketers to ‘mass customize’ relationships. Moreover, the opportunity to build ‘Relationship Marketing’ in India is large because despite being flooded with a constant load of junk emails and telephone calls, the customer still remains open to hearing marketing messages, thus being far more receptive to allow marketers to build a relationship if the message is ‘relevant’.
TECHNOLOGY AND THE BATTLE OF THE LEFT VS RIGHT BRAIN
The market as a function has seen tremendous changes in the last few years. Marketers want to make it easy for consumers to buy their products or experience their service by making them more relevant for customers by profoundly personalizing their products. In the same breath, they want their marketing processes to be more scalable and automated. CMOSs need to ensure that these two goals do not work at cross purposes. AI and new machine learning can play a role here in recognizing customers, identifying their future needs, and scaling findings to large customer bases.
Marketers need both scale and impact, which is why they need a balance between the left and the right brain. Marketing has always been considered a creative function. Over the last few years, there has been this rush of view that almost seem to suggest that marketing is now only Math! ‘Line’ What we need is ‘balanced brain marketing.’ Marketers have always used technology to make powerful 30 second TV commercials, but the function always used this technology with a protective layer of an advertising agency in between. Therefore, technology can make the customer experience meaningful if it is deployed ‘thoughtfully and creatively.’
Technology has now muscled its way into a softer, consumer research-oriented way that marketers had about understanding psychographics, which forms deeper sources of customer knowledge that contain information about a person’s interest, hobbies, attitudes and lifestyle choices. Since, 2012, IBM has compiled a Linguistic Inquiry and Word Count, a psycholinguistic dictionary that uses Twitter as it’s dataset. Using this dataset, Watson, IBM has been able to program a refined set of ‘algorithms’ to sort and retrieve psychographic information form emails, blog, posts, text messages, search histories, etc.
THE NEW NORMAL IN MARKETING
Data in the Centre of Business Models
Google, Amazon, Apple and Facebook are the digital conglomerates of the ‘new age.’ The FAANGs (Facebook, Apple, Amazon, Netflix and Alphabet’ Google) and the BAT (Baidu, Alibaba, and Tencent) lead AI research. These companies have massively invested in a technology that is beginning to give them a huge ‘unfair advantage’, as it provides them with a new strength called ‘data literacy’. Just as we have herbivores and carnivores, now we have companies who have become ‘datastores.’ What they do is, gather online customer data intensively, subject it to sophisticated analyses, and use their learning to improve their business.
This relatively recent recognition of the importance of data has led to the creation of ‘Infonomics,’ a term which describes the act of quantifying, managing ad leveraging information as a formal business asset.
A lot of the new-age companies are asking the customers for their data, secure in their knowledge that they will provide value back to the customer in this barter. One simple example is, Google’s Screenwise Trends panel, which gives a USD 5 cash voucher to anyone willing to share their internet browsing behavior with Google and it’s partners, with a further USD 5 gift every three months after that. However, this is just the beginning. More creative incentives will be developed, from loyalty points to enhanced services that will encourage a customer to share their data. Moreover, the trick lies in making that data the centerpiece in your business model!
Besides, new-age companies have younger employees who are inclined towards using data to change the mind of more senior leaders. The shared economy of data lets owners capitalize on the first part data that they are already gathering from a marketers, site traffic, CRM database or customer purchase history. Google and Apple, through their android and iOS mobile operating systems, respectively have access to the location of every customer’s Wi-Fi-enabled phone. The Silicon Valley giants are not allowing access to such data by outsiders as yet. The new-age companies, from Uber to Facebook, hold growing stores of data about user behaviors, and that is a ‘customer data moat’ that they are creating. As Andrew NG puts it, the software can be replicated but it is difficult to get other’s data. So, data is the defensible barrier for business!
Thus, in today’s machine learning age, access to data sets is even more important than just the data science capability and access to distinct customer data could be leveraged as a strategic advantage.
Learning and Unlearning New Skills
AI will not just automate or augment certain marketing activities. It will also alter how marketing teams work – and will demand marketing executives to learn new skills. As they mature, marketing AI systems will be able to optimize search automatically as well as paid campaigns, and the necessary level of analysis will get done by them. As marketing AI is increasingly able to generate insightful reports or write essential content, marketers will need to leverage their right brains more to tell stories using this data.
Even though the technology is becoming critical for marketers, the question is how well do they understand it. While marketers are turned on by ‘innovation and change’, a typical IT person is turned on by ‘reliability and continuity’. So how do we bridge this gap? Analysts at both Forrester Research and Gartner have independently advocated for a senior-level technology position within the marketing team. Foresters call it the ‘marketing technology office.’ Gartner’s refer to it as a chief marketing technologist.
The IT team can be your best friend if you get the structure right. Marketers who can truly understand the intersection of marketing and technology are rare. Most marketing organizations still struggle to find qualified people to support the evaluation, purchase, implementation, and use of these new marketing technologies.
Location Is the New Pot of Gold!
Location signals are a new and unique set of data now available to marketers, thanks to the mobile phone revolution.
Location is a fantastic predictor of purchase intent. Marketers can look at store visit data and identify customers who may have the intention to buy a particular category of merchandise. Marketers can combine Internet data along with purchase and loyalty data, and use machine learning algorithms to provide additional insights into the customer journey.
We now have over 1.2 billion telecom subscribers. Smartphone users interact with their devices an average of 85 times a day; 46 percent report they could not live without them. In every consumer’s pocket, there is a device, with an array of sensors, for collecting spatial data. This data is incredibly valuable. Moreover, it is getting picked up from a wide variety of sources:- including global positioning satellites, Bluetooth low energy (BLE) beacons, Wi-Fi hotspots, remote sensors, and visible light communication (VLC) sources. The telecom services have technologies that allow them to pinpoint the real-world geographic location of millions of active mobile users – whether through GPS, Wi-Fi hot spot usage or network caller data records (CDRs).
Marketers and Retail Businesses can obtain general demographic information such as age groups in a particular neighborhood from these telcos which allows them to tailor their micro-marketing approach and potentially create new revenue stream based on that. Therefore, the always-on nature of mobile devices coupled with the ability to determine the precise location, give marketers a tremendous ability to create very relevant offerings for consumers.
By 2020, an estimated 50 billion devices will be wirelessly connected to the Internet. Every second, 80 new objects connect to the Internet for the first time. At the same time, from 2012 to 2017, machine to machine traffic has grown an estimated 24 times a compound annual growth rate of 89 percent. CMOs need to start thinking about the data generated from this traffic and it’s customer behavior ramifications.
Creating More In-Depth Personalisation Capabilities
The future of personalization is going to be scary as well as phenomenally impactful for marketers. Devices are getting smaller and more connected. Gartner believes that by 2018, organizations that excel in personalization will outsell companies that do not by 20 percent.
Research shows that a number of connected objects will rise to USD 50- 100 billion by 2020. As we look ahead, microdevices are going to get embedded in us! We may allow healthcare brands to do this but maybe not retail brands. This would enable the patient’s families and doctors to reach out if there are issues while doctors monitor him/her.
Customer Experience at the Heart of Strategy
Marketers will need technology to improve the customer experience, but they will also have to bring in a right-brained creative layer on top of technology to make machine-human interfaces more personalized.
Chatbots have the potential to help here by making the customer experience more comfortable, faster and more satisfying. However, Chatbots will provide superior benefits when they are supported by well thought through decision trees. Writing these decision trees is a role not for technology folks but for marketers who have good business issues as well as a solid understanding of how customers interact with the business. Chatbots will continue to leverage AI to improve their ability to help customers and more calls will be handled automatically in the future with higher customer satisfaction.
Also, it is important for marketers to realize that reducing customer’s effort builds loyalty and according to Mr. Kelkar, maybe, therefore, it is time to have a Customer Effort Score (CES). This was initially presented in an article in the Harward Business Review, CES is based on answers to a single question: ’How much effort did you personally have to put forth to handle your request?’, to which is responded by giving a score on a scale of 1 (very low effort) up to 5 (very high effort).
The Measurement Conundrum
CMOs find it challenging to quantify the return on marketing investment (ROMI) and they struggle with the complexity of attributing which marketing investments have worked.
Internet of Things based solutions will help companies analyze how sales get impacted by customer traffic flows and physical engagement with the product. Low-cost sensors embedded within products will allow that capability to be taken to another level. The ability to capture and analyze a massive amount of data from store videos will further accentuate marketers understanding of customer behavior (at a level of being able to track customer eye movements.) All this will help marketing attribution, and even Facebook and Google will continue to build products that help marketers establish that their digital spending is making a difference to sales.
Such development will also create privacy issues that have to be dealt with in years ahead.
The Privacy Opportunity
People say data is the new oil, but Mr. Kelkar believes, data is the new uranium. It has massive power, but it can be very dangerous because it can be used in ways we do not intend.
The European Union’s (EU’s) General Data Protection Regulation (GDPR), which came into effect in May 2018, empowers citizens to control their own data, and there are signs that similar regulations may spread globally.
Regulations like GDPR may strengthen Facebook and Google’s place in the market, making it onerous for competitors to comply. Every move that Google makes, ostensibly to improve it’s privacy efforts, seems to be actually helping it’s business, for example. removing keywords from referring URLs.
Marketers will need to have a ‘data strategy’ to compete with Facebook and Google supremacy in the market. The best approach in this privacy-first world would be to increase focus on ‘asking customers for more data’ by offering them ‘distinctive value’ through personalization. Brands successfully do this at scale that can build large moats of ‘first-party data’ over time.
Data Sharing to Compete with Amazon-Facebook-Google
The algorithmic and API based business will be the business of the future, and it will change marketing as we know it.
The new-age companies use partnerships through APIs very effectively. Uber uses Google Maps, Twilio, (SMS notifications), Send Grid (emails), and Braintree (payments) to make the magic happen.
There are over 16,784 APIs offered by firms today, according to programmableweb.com. APIs allow different software applications to communicate with each other and exchange data directly, without any human interaction.
BUSINESS MODELS FOR THE DATA ECONOMY: GIVING BACK CONTROL OF CUSTOMER DATA
Companies that transparently inform their customers about the information they gather, give customers control of their personal data and offer fair value in return for it, will be trusted. Consumers will even give such companies more substantial access and a disproportionate share of their wallet. Over time, investors will value this and it will reflect in stock prices too! Over time customers would demand that their personal telecom, credit cards, and banking data can be available to them in a personal data warehouse.
Marketers can help customers lead a better life. Whatever business you are in, customers will want you to add value to their lives. Helping customers use their own data in creative new ways can be a great differentiator. Customer data can be used to benchmark customers. Customers would love to know how their telecom spends compare with someone of a similar profile. Am I spending too much time on the phone lately, that too in my personal hours on official matters? Alternatively, how many hours of kids television does my household watch as compared to others? Customers may willingly provide more data (information about their families or own interests) in return for getting value addition like this.
Companies will create a personal data product business.
THE EVOLVING ROLE OF THE CMO
CMOs who have narrowly defined roles with a predominant focus on Advertising, Market Research, and Brand management will be a minority in days to come.
CMOs will instead have to develop the competency to understand AI and Machine Learning in sufficient detail to decrypt the marketing mumbo jumbo that marketing technology players will be pushing to them at high volume. They will have to understand technology in more depth and ask precisely what AI will do for them and then separate the facts from the fiction.
Marketers will have to develop new competencies to find new data sets to feed the Machine Learning algorithms. The new battlefield lies in the control of the user interface and the customer intelligence system that supports it. Companies that build highly equipped Customer intelligence units will win in the coming days.
