How to Extract Meaning from Competitive Intelligence “Big Data” through External Analytics

In the context of business, specifically competitive intelligence, analytics is the process of taking big data and transforming it into actionable intelligence that leads to positive change. It’s characterized by the following:

  • Big data: Analytics draws inferences from big data, a collection of data that’s too massive for mere mortals to manage.

  • Meaning-based computing: Analytics uses computer technology to draw meaning from data. If you want to know how consumers feel about your products or your organization, for example, you can apply analytics to customer-service e-mails and social media.

  • Dashboards: Analytics software typically displays one or more dashboards that present data in more meaningful formats: charts. A single dashboard may display the daily number of visitors to your website, the number of new and returning visitors, where visitors arrived from, and the bounce rate (the percentage of visitors who arrive at the site and leave without doing anything).

  • Efficient, real-time insight: Analytics performs the work of hundreds of people to present insights immediately as data become available.

Business analytics actually has two applications:

  • Data summaries: Analytics can comprise a simple summary of data organized around a specific area, such as number of visitors to a website and click-through rates (which measure the success of online advertising campaigns).

  • Text analytics: Sometimes referred to as meaning-based computing or statistical-pattern learning, text analytics goes much deeper into a body of structured or unstructured data and can pick up on sentiment, meaning, or other important aspects of the data. Generally, when people use the term analytics today, they’re talking about text analytics.

To wrap your brain around the concepts of big data and text analytics, check out the word cloud created at Tagxedo for “competitive intelligence.” You can tell with a quick glance at this word cloud which words are most commonly associated with the phrase “competitive intelligence” on the web. Analytics is a way of mining and mashing up the meaning of key ideas in a large body of information.

[Credit: Tagxedo]
Credit: Tagxedo

Investors have used analytics to analyze tweets about the stock market and subsequently predict where it was going. Google uses analytics to determine ranking of search-engine results and which ads to display.

Here are a few of the ways that organizations are using big data and external analytics to improve operations:

  • Analyzing millions of social-media messages to gauge customer sentiment about a product in order to develop future improvements

  • Monitoring millions of credit-card transactions to recognize patterns that may indicate fraudulent use of a credit card

  • Narrowing customer segmentation to more precisely tailor products and services to customers

  • Combining analytics with word clouds to provide executives and analysts with an immediate understanding of major themes (such as product problems or complaints) so that immediate action can be taken

  • Monitoring customer-service call centers to understand the real nature of conversations and improve customer satisfaction

How to find for a competitive intelligence analytics solution

Analytics has a wide range of applications that can benefit most areas in an organization. To find out which areas are likely to benefit the most and find solution providers that can meet your needs and budget, take the following steps:

  1. Educate yourself by visiting websites of several of the key players in analytics technology, including the following:

    Visit KD Nuggets for a longer list of analytics providers.

  2. Ask sales representatives from different analytics companies to help you understand how analytics can benefit your organization specifically.

  3. Ask managers in your organization if they’re already using analytics and what they’re using it for.

    Your organization may already be using analytics to track product trends, website efficiency, or a number of other marketing-related issues. If your organization is already using analytics, arrange to have access to information that already exists.

  4. Consult managers and other decision makers to identify areas where external analytics may be beneficial.

    Consider asking managers and other decision makers the following questions:

    • Which competitors do you need more information about?

    • Which technologies do we need to follow?

    • Which future issues that the company is likely to face are the biggest unknowns?

    • What keeps you up at night?

    • Are there any external areas where we currently lack enough intelligence to help us make good decisions?

  5. Develop a comprehensive list of analytics needs for everyone who’s likely to put analytics to good use.

    You need this list when you start shopping for a solution. You want a solution that meets everyone’s needs so all information can be stored, accessed, and managed centrally.

  6. Start shopping for analytics solutions that meet your needs and budget.

    Many of the key players mentioned in Step 1 are also the biggest solution providers.

The best solution isn’t necessarily the one that has the most bells and whistles or the most check marks in its features list. The ideal solution should meet the following criteria:

  • Quick to deploy: Some solutions may take six months to over a year to develop and deploy. Look for a vendor that has a track record of deploying its solution, or at least the most critical modules, in 30 to 60 days.

  • Affordable: You don’t want the cheapest product, but you do need to make sure that the product you choose meets your budget needs.

  • Easy to use: Ease of use is crucial in actually getting people to use the system and getting buy-in from executives and managers. Find out how long it takes the average user to get up to speed on the software.

  • Flexible: Look for a solution that meets your current analytics needs but is flexible enough to accommodate changes down the road. Find out how long it takes to add a new data source and new analytics tools to an existing package.

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