“We are in the business of building [FILL IN THE BLANK], why would we build an insights platform out ourselves.”
That sentiment will drive more and more companies to explore the insights services option. Many already feel like they are chasing a moving target. Data and analytics practices are evolving quickly with new tools and techniques moving the bar higher and higher. Not to mention the explosion of data sources, and the dearth of skilled talent out there. As executives become more aware of the value of data and analytics, they become increasingly dissatisfied with what their organizations can deliver: in 2014 53% of decision-makers were satisfied with internal analytics capabilities but by 2015 those satisfied fell to 42%. These are the leaders who will look for external service providers to deliver insights. They realize they might not get there themselves.
The sentiment expressed in the quote above was actually from a consumer packaged goods company. For its execs winning in cities has become paramount. As urbanization increases, cities provide big opportunities. But not all cities are alike and differentiating what they take to a specific market requires deep local knowledge – and a lot of diverse data. To create hyperlocal, timely, and contextually relevant offers, the company needs data on local news, events, and weather as well as geo-tagged social data. All of that must be combined with its own internal and partner data.
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.
In the age of the customer, customer-obsessed firms serious about personalizing customer experience invest in business intelligence (BI) and analytics tools. Companies collect more and more data on their clients today. BI software is increasingly important to extract information from the raw data, revealing insights. Analytics software tools go beyond traditional reporting and analysis to anticipate customer behavior and provide real-time insights.
The traditional BI market has matured, but still offers a significant growth opportunity. While business intelligence software is not a new product, Forrester projects robust growth for the solution. As we move into the Internet of Things era, an exponential increase in the number of connected devices will drive demand for BI software tools to understand the information. We expect the BI software market to grow at a 9% CAGR over the forecast period.
When Sir Francis Bacon, coined the aphorism "Knowledge is power", he didn’t foresee a 21st century where technology and data science would more automatically and immediately turn knowledge into insight. Today, the phrase “Prediction is Power” may be more appropriate.
There’s no other way to slice it: competition for digital audiences is brutal. Intolerance for poor performance and disengaging experiences drives customers to competitor’s sites more quickly and more permanently than any time in history. Users increasingly demand digital experiences that personalize to their immediate needs and adapt to the current context, not treat them as a market or demographic segment.
In recently published research, we found that even as expectations soar, enterprises are personalizing with methods that are too unsophisticated, too opaque, or too convoluted to meet the complexity and mutability needed to serve individuals. Persona-based segmentation is too simplistic to meet current, much less future, customer expectations. Some solutions provide predictive analytics capabilities but are limited to a few algorithms or black-box methods (e.g. neural networks) are not easily adaptable to new data or scenarios. Those that rely heavily on rules have become morasses, some customers needing to manage and maintain hundreds or thousands of rules to guide digital experiences.
Last year, my colleague Srividya Sridharan published The State Of Customer Analytics 2012 (subscription required). Using the results of her annual customer analytics adoption survey, she uncovered key trends of how customer analytics practitioners use and adopt various advanced analytics across the customer life cycle and highlighted challenges and drivers associated with customer analytics.
This year, I have the pleasure of teaming up with Sri on her yearly survey, to further explore the adoption of advanced analytics, measurement, and attribution. Please read her blog post to learn more about the survey. This survey will explore the adoption and usage of measurement techniques, including attribution, and the adoption of advanced analytics methodologies. With this expanded survey we want to understand how you use and apply measurement and analytics in your organization to optimize both cross-channel marketing campaigns and customer programs.
In particular, we’re fielding questions to understand the goals and challenges associated with measurement and analytics, the adoption and application of measurement and advanced analytics methods, the use of several marketing and customer metrics, the customer insights process and workflow, and the organizational aspects that support measurement and analytics. We encourage you to participate in this survey, as this information will help you benchmark your measurement and analytics adoption efforts.
Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?
Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:
What is the level of importance given to customer analytics in your organization?
Have you clearly defined where you will use the output of customer analytics?
How is your analytics team structured and supported?
How do you manage and process your customer data?
Do you have clear line of sight between analytics efforts and business outcomes?
What is the process of sharing insights from analytics projects?
What type of technology do you need to produce, consume and activate analytics?
The Obama 2012 campaign famously used big data predictive analytics to influence individual voters. They hired more than 50 analytics experts, including data scientists, to predict which voters will be positively persuaded by political campaign contact such as a call, door knock, flyer, or TV ad. Uplift modeling (aka persuasion modeling) is one of the hottest forms of predictive analytics, for obvious reasons — most organizations wish to persuade people to to do something such as buy! In this special episode of Forrester TechnoPolitics, Mike interviews Eric Siegel, Ph.D., author of Predictive Analytics, to find out: 1) What exactly is uplift modeling? and 2) How did the Obama 2012 campaign use it to persuade voters? (< 4 minutes)
The deluge of customer data shows no signs of abating. The perpetually-connected customer leaves data footprints in every interaction with a brand. This presents tremendous opportunities for customer insights professionals and analytics practitioners tasked with analyzing this data, to not only get smarter about customers but ensure that the insights get appropriately used at the point of customer interaction.
When we asked customer analytics users about the challenges and drivers of customer analytics adoption, we found that data integration and data quality continue to inhibit better adoption of customer analytics while users still want to use analytics to improve the data-driven focus of the organization and drive satisfaction and customer retention.
Forrester’s Customer Analytics Playbook guides customer insights professionals, marketing scientists and customer analytics practitioners into this new reality of customer data and helps discover analytics opportunities, plan for greater sophistication, take steps towards building a customer analytics capability and continually monitor progress of analytics initiatives. It will include 12 chapters (and an executive overview) that cover different aspects of customer analytics.
I just received yet another call from a reporter asking me to comment on yet another BI vendor announcing R integration. All leading BI vendors are embedding/integrating with R these days, so I was not sure what was really new in the announcement. I guess the real question is the level of integration. For example:
Since R is a scripting language, does a BI vendor provide point-and-click GUI to generate R code?
Can R routines leverage and take advantage of all of the BI metadata (data structures, definitions, etc.) without having to redefine it again just for R?
How easily can the output from R calculations (scores, rankings) be embedded in the BI reports and dashboards? Do the new scores just become automagically available for BI reports, or does somebody need to add them to BI data stores and metadata?
Can the BI vendor import/export R models based on PMML?
Is it a general R integration, or are there prebuilt vertical (industry specific) or domain (finance, HR, supply chain, risk, etc) metrics as part of a solution?
What server are R models executed in? Reporting server? Database server? Their own server?
Then there's the whole business of model design, management, and execution, which is usually the realm of advanced analytics platforms. How much of these capabilities does the BI vendor provide?
Did I get that right? Any other features/capabilities that really distinguish one BI/R integration from another? Really interested in hearing your comments.