The first U.S. presidential debate was the most watched in history, with 84 million people tuning in. Sure, many of us wanted to educate ourselves before practicing our solemn duty as democratic citizens in November. However, many of us also didn’t want to miss out on what (hopefully) promised to be a once in a lifetime political event . We were motivated by FOMO.
Last week, I had the opportunity to attend a teleconference highlighting IBM Watson’s success stories over the past year. Most of them are under NDA, so I can’t go into the details, but I will say they covered an incredibly broad range of use cases. One use case that I was hoping they would cover and didn’t was content analytics for marketing, aka “quantent.”
In the customer analytics arena, we often talk about “getting the right message to the right customer at the right time.” This is only partly true. Well-built and rigorously tested propensity models will deliver you the right customer and the right time. Behavioral segmentation models may even specify the best channel to use to deliver the message. But that still leaves the message itself. Whatis the right message?
Content analytics begins with entirely different data than customer analytics, and the two analytical streams merge just prior to the point of action. Whereas customer data contains information about customer profiles, transactions, and behaviors, data about content characterizes tone, length, wording, dates, products mentioned, type of offer (if applicable), and other key themes within the content itself. Most importantly, content that has been subject to A/B testing also creates data about the success of the message on an individual customer basis.
We have all this valuable data about our customers, but we need to make better use of it.
This is the most common theme I hear on inquiry calls, at conferences, and in advisory sessions. At this point, companies are fully aware that their data contains enormous value. In fact, I like to think that data has a potential value much like the concept of potential energy in physics. In physics, the conversion of potential energy to kinetic energy requires force. In business, customer analytics is the Force that unlocks the hidden value in your customer data.
Because customer analytics often relies on advanced machine learning algorithms, it used to be the domain of statisticians who could write code in R or Python. Today, thanks to the 11 customer analytics solution providers in The Forrester Wave™: Customer Analytics Solutions, Q1 2016, customer insights professionals are applying these techniques to their data to address key business objectives. This report, which is only available to Forrester clients, evaluates the customer analytics solutions of Adobe, AgilOne, Angoss, Alteryx, FICO, IBM, Manthan, Pitney Bowes, SAP, SAS, and Teradata.
Greetings! My name is Brandon Purcell and I am the new Senior Analyst serving Customer Insights professionals at Forrester. I will cover customer analytics which uses advanced analytics to analyze customer data to optimize customer-focused programs and initiatives to drive acquisition, retention, cross-sell/upsell, loyalty, personalization, and contextual marketing. I am a recovering customer analytics practitioner and come to Forrester from a boutique consulting firm where I led a team of data scientists that helped our clients solve their urgent business challenges by harnessing the latent value in their customer data. A few highlights from my former life:
I helped develop a best in class Voice of the Customer program at one of the country’s largest banks
I created and led many trainings in business applications of predictive analytics
I built a patented algorithm that uses geospatial data to predict a person’s future location
I attended Huawei’s 2015 global analyst summit in Shenzhen last week and studied its latest strategy for big data innovation. In a change from its previous big data offerings around storage, Huawei has reorganized the data analytics department and focused on infrastructure software that enables big data applications from ISV partners. Mr. Zhu, General Manager of Huawei FusionInsight, talked about FusionInsight, which financial institutions like ICBC and China Merchants Bank use to enhance customer analytics capabilities like customer recognition, segmentation, and marketing automation. Basically, Huawei FusionInsight is a data analytics platform with two major components: 1) a distributed open “database” platform that includes Hadoop, Sparc, and Storm and 2) “middleware” with open APIs to enable multisource data management and analytics.
Chinese financial institutions have a huge amount of legacy transactional data as well as in-motion online and mobile banking data, but they are unable to deal with all of it. With the previous systems of record, financial institutions couldn’t analyze all of this structured and semi-structured data in a unified “data pool.” To solve this problem, they are using Huawei FusionInsight to consolidate multisource data and enable more efficient customer and marketing analytics. Huawei FusionInsight is creating new value in the customer journey for a leading Chinese commercial bank by allowing it to:
Retaining and delighting empowered customers requires continuous, technology-enabled innovation and improved customer insight (CI). The logic is simple in theory, but that doesn’t make it any easier to implement in practice.
In my recent report, entitled “Applying Customer Insight To Your Digital Strategy”, I highlight the top lessons learned from organizations in Asia Pacific (AP) that are successfully leveraging CI to fuel digital initiatives. It all starts by ensuring that data-driven decision-making is central to the digital strategy. With that in mind, I want to use this blog post to focus on two key lessons from the report:
Lesson One: Establish A Clear Mandate To Invest In Customer Analytics
Successful companies serve empowered customers in the way they want to be served, not the way the company wants to serve them. When building a mandate you should:
■ Expect natural tensions between various business stakeholders to arise. To secure buy-in from senior business decision-makers, start by illustrating the clear link between digital capabilities and data as a source of improved customer understanding. Identify measurable objectives and then link them to three to four scenarios that highlight where the biggest opportunities and risks exist. Continue to justify data-related investments by restating these scenarios at regular intervals.
If you are excited about challenging thinking and leading change for our clients in customer analytics and enterprise marketing technologies, we’d love to hear from you. We have two open Customer Insights analyst positions to focus on these critical coverage areas – customer analytics and enterprise marketing technologies. You will write research for, present to, and advise Customer Insights Professionals to help guide their customer data, analytics, and marketing technology decisions.
In my last blog post I outlined Forrester’s key customer insights (CI) predictions for 2015. Now I’d like to drill down into some of the key barriers to CI effectiveness we’re seeing among Asia Pacific-based organizations. This content was pulled from my recently published report, which Forrester clients can access here.
Core competencies of effective CI pros have typically centered on customer segmentation and campaign performance measurement. When extending these capabilities to digital marketing strategies, the goal is typically to enable more effective customer acquisition and onboarding by extending reach. In other words, digital innovation often simply means “better campaigns.”
But what happens once that process is complete? It’s not enough to have a world-class digital capability for acquiring new customers. Empowered customers expect the same type of seamless experience, improved efficiency, and heightened responsiveness in all subsequent interactions with your brand.
So why so many firms struggling to realize the full potential of customer analytics to effectively serve and retain their customers? I’ll give you four reasons:
Uber faces fierce competition in China from local taxi hailing service providers Didi and Kuaidi Taxi, which both launched Uber-style e-hailing services in 2014. Both providers use a costly subsidy model to entice taxi users to switch to e-hailing services. Kuaidi Taxi, which recently received $700 million in Series D funding to buy more self-owned e-hailing vehicles, has hired more drivers and continues to provide subsidies. Uber has a smaller user base than either Didi or Kuaidi and limited funds that it can leverage — so to win customers in China, Uber must engage customers differently. Uber can leverage its global organization’s existing customer analytics strategy and tools to better understand their (potential) customers and engage with them throughout the customer life cycle.
On New Year’s Eve 2014/2015, it was predicted that taxi service would be unobtainable as people concentrated on the New Year countdown. Uber analyzed historical customer data and was able to provide more appealing e-hailing options than Didi’s and Kuaidi’s cash coupons. Uber contacts customers in advance and asks them to confirm any rate increases due to its dynamic pricing model; this helps to set the correct expectations with customers about fares:
China has experienced a fast expansion of credit card usage in the past 10 years, accumulating more than 390 million credit cards by the end of 2013, around 16 times more than 2003. But Chinese banks suffered from low activation rates of credit cards. In my recent report, I found China CITIC Bank (CNCB) faced a similar challenge; their 21 million credit cards had less than 20% activation before 2012.
In 2012, to increase the number of active credit card users, CNCB decided to revamp its customer analytics capabilities to better understand customer profiles and manage customer relationships. As a first step, the bank used SAS Enterprise Miner to deeply analyze both active and inactive cardholders and their usage scenarios and to measure the effectiveness of its credit card campaigns and programs through cardholder analysis for customer segmentation and marketing program effectiveness analysis including:
Cardholder analysis for customer segmentation.CNCB first collected and classified basic information about its cardholders from past marketing campaigns and transactional data. It defined four basic types of cardholders: inactive users, moderate users, convenience users, and heavy users. The bank spent two months to build data marts from the summarized data. It decided to focus on two groups of inactive cardholders: those who could be swayed by marketing campaigns and those who were heavy users of other banks’ cards but not CNCB’s through the analytics engine.