Posts tagged KPI
Key Driver Analysis: Top-down & Bottom-up Approach

Text Analytics Tips - Branding Get a complete picture of your data: The ‘Top-Down and Bottom-Up Approach’

At OdinText we’ve found that the best way to identify all key drivers in any analysis really, especially in customer experience management (including but not limited to KPI’s such as OSAT, Net Promoter Score, Likelihood to Return or other real behavior) is through a dual process combining a theory-driven (aka “top-down”) and a data-exploratory or data-driven approach (aka “bottom-up”):


This approach requires you to identify important concepts or themes before even starting to explore and analyze your data. In customer satisfaction or brand equity research you can often start by identifying these key concepts by reviewing the strengths and weaknesses associated with your brand or product, or by listing the advantages and challenges that you believe may be prevalent (e.g., good customer service, poor management, professionalism etc.). This is an a priori approach where the user/analyst identifies a few things that they believe may be important.


This approach requires you to use a more advanced text analytics software, like OdinText, to mark and extract concepts or themes that are most frequently mentioned in customers’ text comments found in your dataset and that are relevant to your brand or product evaluation (e.g., high cost, unresponsiveness, love). Better analytics software should be able to automatically identify important things that the user/analyst didn’t know to look for.

Top-down vs. Bottom-up

The top-down approach does not reflect the content of your data, whereas the bottom-up approach while being purely based on the data can fail to include important concepts or themes that occur in your data less frequently or is abstracted in some way. For instance, in a recent customer satisfaction analysis, very few customer comments explicitly mentioned problems associated with management of the local branches (therefore, “management” was not mentioned frequently enough to be identified as a key driver by the software using the bottom-up approach).

However as the analyst had hypothesized that management might be an important issue, more subtle mentions associated with the concept of management were included in the analysis. Subsequently predictive analytics revealed that “poor management” was in fact a major driver of customer dissatisfaction. This key driver was only “discovered” due to the fact that the analyst had also used a top-down approach in their text analysis.

It may be that some of the concepts or themes identified using the two approaches overlap but this will only ensure that the most important concepts are included.

Remember, that only when combining these two very different approaches can you confidently identify a complete range of key drivers of satisfaction or other important metrics.

I hope you found today’s Text Analytics Tip useful.

Please check back in the next few days as we plan to post a new interesting analysis similar to, but even more exciting than last week’s Brand Analysis.


Text Analytics Tips with Gosi


[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior.  Please feel free to request additional information or an OdinText demo here.]

New to Text Analytics?

6 lessons for CIOs and CMOs who are new to text analytics(Free White Paper)

Unstructured data is the most prevalent form of information on the planet. It also underpins much of our communication. It exists in our e-mails, surveys, social media accounts, call center logs, etc. With a strong text analytics strategy in place, companies can get critical information from this data to drive better business decisions.

You may recall my blog interviews with several client side analytics managers ahead of this years’ Useful Business Analytics Summit.

Data Driven Business has compiled a free white paper which focuses on the business benefits (and challenges!) of text analytics from the perspectives of 4 of these experts from Highmark Health, Toyota, Mozilla, and Visa (All who are slated to speak at the 13th Annual Text Analytics Summit West on November 4-5 in San Francisco.

You can download the free whitepaper here:

Some of the issues covered include:

· 6 lessons for CIOs and CMOs who are new to text analytics

· Identifying KPIs for your text analytics initiatives

· Barriers to adoption and how to overcome them

Big thanks to the following contributors:

Mark Pitts, Vice President, Enterprise Informatics, Data & Analytics at Highmark Health

Farouk Ferchichi, Executive Director at Toyota Financial Services

Matthew P.T. Ruttley, Manager of Data Science at Mozilla Corporation

Ramkumar Ravichandran, Director, Analytics at Visa


[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]

The Text Analytics Opportunity

The Text Analytics Opportunity Text Analytics remains an opportunity for those wishing to gain an information advantage

[Note: This is an ongoing series of interviews on analytics ahead of the Useful Business Analytics Summit. Feel free to check out the rest of the interviews beginning here]


My favorite topic of any analytics conference is of course the mining and analysis of unstructured data. Whether you call it Natural Language Processing (NLP), Text Mining, or the more recently popular Text Analytics chances are you’ve heard of it. Since I started Anderson Analytics ten years ago, as the first consumer insights firm to leverage text analytics, the discipline seems to have gone from an unknown to mainstream ubiquitousness.

That said, because it is such a quickly evolving field many of the main players 10 years ago have faded into relative obscurity. Some have been purchased by other companies, many have simply not been able to keep up with advancements or proven adequate value.

Therefore perhaps I shouldn’t have been surprised that this was the one area where our analytics experts were a bit less sure of themselves, and I received relatively few responses to my questions.

That said, consensus is that there certainly are text analytics software options on the market that do provide strong value. Personally I think the main challenge is that there are still too few analysts with experience in text analytics, and too little time allocated to prove just how amazing unstructured data insights can be!

Q. What is your opinion on the current state of the unstructured/text analytics field?



I am the wrong guy to ask, because I was already blown away six years ago when Attensity was boiling down conversations into subject-verb pairs, and things have only gotten better since then. I think there’s a point in the life of each



At this moment, I believe for the kind of experiments that people have started to leverage it for, it is good enough.



For me personally its more supplemental data. Structured data is easier to utilize, slice and dice.

Unstructured data might be very useful resource of qualitative data and supplemental to quantitative analysis.

Also there are tools that can create structured analytics from unstructured.



A lot of solutions exist in the market place but it is a complex problem and we have a long ways to go.


Q. What if anything in text analytics have you found that really works well? What doesn’t?



I don’t have direct experience with text mining beyond what we’ve done with Attensity.



What works well is the flexibility and ability to change and implement once you have the engine built. What doesn’t work well is the overpriced text analytics tools, which makes many, develop their own and miss the opportunity to focus on analytics instead of transforming the unstructured data.



Works well: Qualitative data, opinion based data.

Doesn’t : Certain KPIs without benchmark



High level sensitivity analysis and high-level signaling works well. But the solutions are not at a place for granular actionable insights. In other words, use them as an indicator and not as an actionable solutions.


Stop by for the next blog post as I ask our experts about tips for selecting a software vendor, how much software should cost. I’ll even be asking how our client side speakers like to be sold to…





[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™ software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]

Are All Data Created Equal?

AllDataNotCreatedEqual A tweet, a transaction, an email or a phone call - Do you have a preference?

[Note: This is an ongoing series of interviews on analytics ahead of the Useful Business Analytics Summit. Feel free to check out the rest of the interviews beginning here]

I thought this was an important question, and one I knew the answer to. My thinking, based on experience has been, certainly not, some data is far richer and more important than other data. For instance 1 or 10,000 tweets for that matter are no where near as important as one good data record of an actual customer calling or emailing your customer service center with a specific complaint, praise or suggestion.

That said as I posed the question to our panel of client side analytics experts I began to think maybe the question itself made was a mistake. The all too common mistake of putting the data before the question.

Curious to hear your thoughts. Can we legitimately ask this question about data without first answering the question of what question is to be answered? And if we can, on what side of the spectrum do you fall – all data is created equal OR some data are priceless and others are almost useless?




[Thomas Speidel - Suncor Energy]

 It depends on what we are trying to find out. For mission critical decisions, it's important to have data that was intentionally captured for that or a similar purpose (usually structured).

For exploration or low consequence questions, any data will do so long as we understand the limitations of our findings.


[Sofia Freyder – MasterCard]

I think all data is important: structures and unstructured, quantitative and qualitative, on- line and off-line, behavioral or opinion based. Each specific situation will define which data should be considered more accurate and precise.


[Deepak Tiwari - Google]

It depends. We use all types of data (structured, unstructured) and depending on the problem use them to varying degree.


[Jonathan Isernhagen – Travelocity]

 I’m a finance guy at heart, and believe in the idea of net present value….the idea that every allocation decision we make can be thought of as a project that should pay out more than the investment. I’m interested in any data which directly inform such “project” decisions…the ROI stuff . I’m less interested in other data. There’s a school of thought that I’d call “Pathism” or “Funnelism” which rejects channel attribution. If you don’t have the marketing budget to justify investing in an algorithmic attribution model, that’s one thing. If you imagine that knowing your fourth-most-popular path to conversion is SEO-to-Direct is better than knowing your individual channel ROIs….I would beg to differ.


[Farouk Ferchichi - Toyota Financial Services]

I don’t believe there is data that is not important. All data is important given the appropriate context. Internal and external structured data in the form of financials or customers’ data is important to analyze histories and develop models but internal and external unstructured data is equally as important to discover and access new type of information. The question becomes how to access data and what to acquire/store and for that you need a data discovery and acquisition strategy.


[Anthony Palella - Angie's List]

- Importance is determined by the high value questions that need to be answered. When I start working with a business partner, I don't ask about KPI's. I ask, "What are the 10-12 questions you need answers to in order to successfully run your business?". The data needed to answer these questions is "important".


[Larry Shiller - Yale]

This is a "meta" answer... "Type" means a way to slice and dice. If you are slicing data only one way, that way may be a shiny object: Look for other ways (i.e., other dimensions) to slice your data. For example, the most common dimension is time: Look for other dimensions/pivots.


Thanks to our speakers at the upcoming Useful Business Analytics Summit for their thoughtful answers to the above question. This Q&A is part of an ongoing series focusing on big data and business analytics in general. Feel free to check out some of our past questions on Big Data, How to Keep Up to Date on Analytics, Top 10 Analytics Tips. Our next post will be on my favorite topic, text analytics!





[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of a patented Next Generation approach to text analytics known as OdinText. For more information and to inquire about software licensing visit OdinText INFO Request.]