Category Archives: Analytics

Why search and Findability is critical for the customer experience and NPS on websites

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To achieve a high NPS, Net Promoter Score, the customer experience (cx) is crucial and a critical factor behind a positive customer experience is the ease of doing business. For companies who interact with their customers through the web (which ought to be almost every company these days) this of course implies a need to have good Findability and search on the website in order for visitors to be able to find what they are looking for without effort.

The concept of NPS was created by Fred Reichheld and his colleagues of Bain and Co who had an increasing recognition that measuring customer satisfaction on its own wasn’t enough to make conclusions of customer loyalty. After some research together with Satmetrix they came up with a single question that they deemed to be the only relevant one for predicting business success “How likely are you to recommend company X to a friend or colleague.” Depending upon the answer to that single question, using a scale of 0 to 10, the respondent would be considered one of the following:

net-promoter

The Net Promoter Score model

The idea is that Promoters—the loyal, enthusiastic customers who love doing business with you—are worth far more to your company than passive customers or detractors. To obtain the actual NPS score the percentage of Detractors is deducted from the percentage of Promoters.

How the customer experience drives NPS

Several studies indicate four main drivers behind NPS:

  • Brand relationship
  • Experience of / satisfaction with product offerings (features; relevance; pricing)
  • Ease of doing business (simplicity; efficiency; reliability)
  • Touch point experience (the degree of warmth and understanding conveyed by front-line employees)

According to ‘voice of the customer’ research conducted by British customer experience consultancy Cape Consulting the ease of doing business and the touch point experience accounts for 60 % of the Net Promoter Score, with some variations between different industry sectors. Both factors are directly correlated to how easy it is for customers to find what they are looking for on the web and how easily front-line employees can find the right information to help and guide the customer.

Successful companies devote much attention to user experience on their website but when trying to figure out how most visitors will behave website owners tend to overlook the search function. Hence visitors who are unfamiliar with the design struggle to find the product or information they are looking for causing unnecessary frustration and quite possibly the customer/potential customer runs out of patience with the company.

Ideally, Findability on a company website or ecommerce site is a state where desired content is displayed immediately without any effort at all. Product recommendations based on the behavior of previous visitors is an example but it has limitations and requires a large set of data to be accurate. When a visitor has a very specific query, a long tail search, the accuracy becomes even more important because there will be no such thing as a close enough answer. Imagine a visitor to a logistics company website looking for information about delivery times from one city to another, an ecommerce site where the visitor has found the right product but wants to know the company’s return policy before making a purchase or a visitor to a hospital’s website looking for contact details to a specific department. Examples like these are situations where there is only one correct answer and failure to deliver that answer in a simple and reliable manner will negatively impact the customer experience and probably create a frustrated visitor who might leave the site and look at the competition instead.

Investing in search have positive impacts on NPS and the bottom line 

Google has taught people how to search and what to expect from a search function. Step one is to create a user friendly search function on your website but then you must actively maintain the master data, business rules, relevance models and the zero-results hits to make sure the customer experience is aligned. Also, take a look at the keywords and phrases your visitors use when searching. This is useful business intelligence about your customers and it can also indicate what type of information you should highlight on your website. Achieving good Findability on your website requires more than just the right technology and modern website design. It is an ongoing process that successfully managed can have a huge impact on the customer experience and your NPS which means your investment in search will generate positive results on your bottom line.

More posts on this topic will follow.

/Olof Belfrage

Predictive Analytics World 2012

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At the end of November 2012 top predictive analytics experts, practitioners, authors and business thought leaders met in London at Predictive Analytics World conference. Cameral nature of the conference combined with great variety of experiences brought by over 60 attendees and speakers made a unique opportunity to dive into the topic from Findwise perspective.

Dive into Big Data

In the Opening Keynote, presented by Program Chairman PhD Geert Verstraeten, we could hear about ways to increase the impact of Predictive Analytics. Unsurprisingly a lot of fuzz is about embracing Big Data.  As analysts have more and more data to process, their need for new tools is obvious. But business will cherish Big Data platforms only if it sees value behind it. Thus in my opinion before everything else that has impact on successful Big Data Analytics we should consider improving business-oriented communication. Even the most valuable data has no value if you can’t convince decision makers that it’s worth digging it.

But beeing able to clearly present benefits is not everything. Analysts must strive to create specific indicators and variables that are empirically measurable. Choose the right battles. As Gregory Piatetsky (data mining and predictive analytics expert) said: more data beats better algorithms, but better questions beat more data.

Finally, aim for impact. If you have a call center and want to persuade customers not to resign from your services, then it’s not wise just to call everyone. But it might also not be wise to call everyone you predict to have high risk of leaving. Even if as a result you loose less clients, there might be a large group of customers that will leave only because of the call. Such customers may also be predicted. And as you split high risk of leaving clients into “persuadable” ones and “touchy” ones, you are able to fully leverage your analytics potencial.

Find it exciting

Greatest thing about Predictive Analytics World 2012 was how diverse the presentations were. Many successful business cases from a large variety of domains and a lot of inspiring speeches makes it hard not to get at least a bit excited about Predictive Analytics.

From banking and financial scenarios, through sport training and performance prediction in rugby team (if you like at least one of: baseball, Predictive Analytics or Brad Pitt, I recommend you watch Moneyball movie). Not to mention Case Study about reducing youth unemployment in England. But there are two particular presentations I would like to say a word about.

First of them was a Case Study on Predicting Investor Behavior in First Social Media Sentiment-Based Hedge Fund presented by Alexander Farfuła – Chief Data Scientist at MarketPsy Capital LLC. I find it very interesting because it shows how powerful Big Data can be. By using massive amount of social media data (e.g. Twitter), they managed to predict a lot of global market behavior in certain industries. That is the essence of Big Data – harness large amount of small information chunks that are useless alone, to get useful Big Picture.

Second one was presented by Martine George – Head of Marketing Analytics & Research at BNP Paribas Fortis in Belgium. She had a really great presentation about developing and growing teams of predictive analysts. As the topic is brisk at Findwise and probably in every company interested in analytics and Big Data, I was pleased to learn so much and talk about it later on in person.

Big (Data) Picture

Day after the conference John Elder from Elder Research led an excellent workshop. What was really nice is that we’ve concentrated on the concepts not the equations. It was like a semester in one day – a big picture that can be digested into technical knowledge over time. But most valuable general conclusion was twofold:

  • Leverage – an incremental improvement will matter! When your turnover can be counted in millions of dollars even half percent of saving mean large additional revenue.
  • Low hanging fruit – there is lot to gain what nobody else has tried yet. That includes reaching for new kinds of data (text data, social media data) and daring to make use of it in a new, cool way with tools that weren’t there couple of years ago.

Plateau of Productivity

As a conclusion, I would say that Predictive Analytics has become a mature, one of the most useful disciplines on the market. As in the famous Gartner Hype, Predictive Analytics reached has reached the Plateau of Productivity. Though often ungrateful, requiring lots of resources, money and time, it can offer your company a successful future.

Tutorial: Optimising Your Content for Findability

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This tutorial was done on the 6th of November at J. Boye 2012 conference in Aarhus Denmark. Tutorial was done by Kristian Norling.

Findability and Your Content

As the amount of content continues to increase, new approaches are required to provide good user experiences. Findability has been introduced as a new term among content strategists and information architects and is most easily explained as:

“A state where all information is findable and an approach to reaching that state.”

Search technology is readily used to make information findable, but as many have realized technology alone is unfortunately not enough. To achieve findability additional activities across several important dimensions such as business, user, information and organisation are needed.

Search engine optimisation is one aspect of findability and many of the principles from SEO works in a intranet or website search context. This is sometimes called Enterprise Search Engine Optimisation (ESEO). Getting findability to work well for your website or intranet is a difficult task, that needs continuos work. It requires stamina, persistence, endurance, patience and of course time and money (resources).

Tutorial Topics

In this tutorial you will take a deep dive into the many aspects of findability, with some good practices on how to improve findability:

  • Enterprise Search Engines vs Web Search
  • Governance
  • Organisation
  • User involvement
  • Optimise content for findability
  • Metadata
  • Search Analytics

Brief Outline

We will start some very brief theory and then use real examples and also talk about what organisations that are most satisfied with their findability do.

Experience level

Participants should have some intranet/website experience. A basic understanding of HTML, with some previous work with content management will make your tutorial experience even better. A bonus if you have done some Search Engine Optimisation (SEO) for public websites.

Analyzing the Voice of Customers with Text Analytics

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Understanding what your customer thinks about your company, your products and your service can be done in many different ways. Today companies regularly analyze sales statistics, customer surveys and conduct market analysis. But to get the whole picture of the voice of customer, we need to consider the information that is not captured in a structured way in databases or questionnaires.

I attended the Text Analytics Summit earlier this year in London and was introduced to several real-life implementations of how text analytics tools and techniques are used to analyze text in different ways. There were applications for text analytics within pharmaceutical industry, defense and intelligence as well as other industries, but most common at the conference were the case studies within customer analytics.

For a few years now, the social media space has boomed as platforms of all kinds of human interaction and communication, and analyzing this unstructured information found on Twitter and Facebook can give corporations deeper insight into how their customers experience their products and services. But there’s also plenty of text-based information within an organization, that holds valuable insights about their customers, for instance notes being taken in customer service centers, as well as emails sent from customers. By combining both social media information with the internally available information, a company can get a more detailed understanding of their customers.

In its most basic form, the text analytics tools can analyze how different products are perceived in different customer groups. With sentiment analysis a marketing or product development department can understand if the products are retrieved in a positive, negative or just neutral manner. But the analysis could also be combined with other data, such as marketing campaign data, where traditional structured analysis would be combined with the textual analysis.

At the text analytics conference, several exciting solutions where presented, for example an European telecom company that used voice of customer analysis to listen in on the customer ‘buzz’ about their broadband internet services, and would get early warnings when customers where annoyed with the performance of the service, before customers started phoning the customer service. This analysis had become a part of the Quality of Service work at the company.

With the emergence of social media, and where more and more communication is done digitally, the tools and techniques for text analytics has improved and we now start to see very real business cases outside the universities. This is very promising for the adaptation of text analytics within the commercial industries.

Video: Search Analytics in Practice

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Search Analytics in Practice from Findwise on Vimeo.

This presentation is about how to use search analytics to improve the search experience. A small investment in time and effort can really improve the search on your intranet or website. You will get practical advice on what metrics to look at and what actions can be taken as a result of the analysis.

Video in swedish “Sökanalys i praktiken”.

The presentation was recorded in Gothenburg on the 4th of May 2012.

The presentation featured in the video:

Search Analytics in Practice

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Architecture of Search Systems and Measuring the Search Effectiveness

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Lecture made at the 19th of April 2012, at the Warsaw University of Technology. This is the 9th lecture in the regular course for master grade studies, “Introduction to text mining”.

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Analytics and Big Data at IBM Information On Demand 2011

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The big trend these days are in Big Data and how you can analyze large amounts of information in order to gain important insights, and from those insights be able to take the right action. This trend was a hot topic at the IBM Information On Demand (IOD) conference in Las Vegas earlier this year. IBM has a very strong position in this field, it’s hard to have missed how their computer system Watson challenged the top players of all time in Jeopardy recently, and won! Read more about Watson

Now IBM has taken the technology behind Watson and started to apply it in their different analytics products, where one specific area that is being targeted is healthcare. For this area IBM released a new product during IOD called IBM Content and Predictive Analytics for Healthcare, which can for example be used as a tool for physicians to support them in their diagnosis of patients.

In April this year IBM merged two of their products, their search engine OmniFind and their product for analyzing large amounts of unstructured information, Content Analytics. The new product is called IBM Content analytics with Enterprise search and it too is based on much of the same technology that is used in Watson, more specifically it utilizes the same Natural Language Processing techniques. This means that it has the ability to understand text on a level just as sophisticated as that of Watson.

Content Analytics with enterprise search scales very well to many millions of documents. However, when there is a need for analyzing really enormous data sets, in the magnitude of petabytes or even exabytes, IBM has developed what they call their BigData platform. This platform mainly revolves around two products, InfoSphere Streams and InfoSphere BigInsights, and it builds on a foundation of open source software, such as Apache Hadoop and Apache Lucene. InfoSphere Streams is used for real time analysis of information in motion. This helps you understand what’s happening right at this moment in your organization and supports you in taking appropriate action as things are happening. InfoSphere BigInsights on the other hand lets you analyze and draw insight from massive amounts of already existing data.

Studies have shown how organizations that fall short in this area are overtaken by those who understand how to use the power of analytics.

IBM has surely chosen an interesting path when merging Analytics with Findability.