How do 20% of Marketers Outperform the Rest?

how do 20% of marketers outperform the restA recent IBM study of global marketers found 20% outperform the rest? Of course, this begs the question of

how do 20% of marketers outperform the rest and

how to I become one of the 20%?

The effective 20%

Let’s start by looking what the 20% do differently from the other 80% of marketers

  1. Products and services – functionality, brand, packaging  —- 160% more effective
  2. Promotion – advertising, marketing, sales ——————— 130% more effective
  3. Place – distribution, logistics, channels ————————- 160% more effective
  4. Price – list price, bundling, discounts ————————— 180% more effective

So, maybe you’re asking yourself, why do I care if they’re more effective with the 4Ps, I’m doing OK. Well, hold on to that thought as I share more about the report.

The top 20% of marketers enjoy:

  1.  100% higher CLV (customer lifetime value)
  2. 30% increased customer retention
  3. 80% higher buying intentions
  4. 40% increase in brand image
  5. and 11% higher net sales!!!!!!

20% of marketers are taking their improved effectiveness TO THE BANK!

What the 20% do different?

How do 20% of marketers outperform the rest? Simple — they sweat the hard stuff. High achievers are much more likely that the rest of marketers to:

  • systematically assess customer satisfaction. They’re laser focused on improving the customer experience.
  • monitor delivery commitments to improve on-time delivery. Superior firms communicate with suppliers and vendors to provide information about customer demand.
  • identify up selling and cross-selling opportunities for sales and other forward facing staff
  • train sales and customer service staff on product lines, capabilities, and promises. Effective brands identify gaps between promises and delivery with an eye toward reducing or eliminating the gap. Effective brands collaborate internally and externally with other brand actors to ensure consistent delivery of brand value. They align and ensure brand understanding throughout the organization to ensure the brand delivers on its value promises and provides satisfaction.
  • design marketing offers to the point of purchase. Successful brands create and deliver brand messages at EVERY point in the customer interaction, including at the point of purchase. Similarly, they provide marketing offers and customer care messages during service and support actions after the sale.

Analytics critical for understanding how 20% of marketers outperform the rest

Digging deeper into who 20% of marketers outperform the rest, you see the single factor underpinning their success is analytics — well and a deep commitment to being customer-centric.

Here are just a few examples of how the 20% use analytics to support their effectiveness:

  1. Response modeling — if I do X, how will customers/ prospects react. For example, if I add a new benefit to my product, how many more units will I sell?
  2. Attribution modeling — what caused customers/ prospects to do X. For instance, if I ran an ad, how many inquiries, product demonstrations, and sales came from the ad?
  3. CLV modeling to reflect the net present value of future costs and revenue associated with current customers or classes of customers.
  4. Churn — which reflects how many customers will leave over a given time period

How do you apply analytics to be one of the 20%?

Notice, all the analytics identified above that contribute to the improved marketing performance of 20% of marketers are PREDICTIVE ANALYTICS. That means you must go beyond simple descriptive statistics showing how much, how many, how often. According to Forbes, predictive analytics build inferences (models) to predict future behaviors. In the case of marketing, predictive analytics predict responses to marketing campaigns, website elements, and promotional offers base on past behaviors.

Predictive analytics not only requires effective tools to COLLECT meaningful descriptive data, but a skilled business intelligence analyst capable of interpreting descriptive analytics and turning it into actionable decisions and building predictive models to further aid in decision-making.

You’ll likely need someone in the C-suite to champion the use of predictive analytics, also according to Forbes.

Need Help?

So, now you know what you have to do to be part of the 20% of marketers who outperform the rest. Whether you need a complete analytics strategy, some help with Adwords, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.


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Social Media Analytics: Predictive Analytics

predictive analyticsPredictive analytics have been around a long time in finance and economics.  Slowly, these analytic tools are finding their way into the marketing and social media arenas.

What are predictive analytics?

Predictive analytics use data regarding past behaviors to predict how individuals will behave in the future.

For instance, your credit score is a predictive model including your repayment history and other information to predict whether you’re a good credit risk or not.

Predictive models commonly include a number of variables, such as # of late payments, and weighting factors that reflect the importance of that variable in predicting future behavior.  These are commonly regression-type models.

Modern predictive analytics use similar data and build similar models to predict how groups of people will behave, in general, or classify individuals into such groups.  For instance, we might build a model that predicts how much of a product we’ll sell if we lower (or raise) the price.  While we won’t be able to predict WHO will buy at the new price, we really don’t care.  We only need to know if we’ll sell more at the new price.  Thus, predictive analytics help us determine which marketing strategies will produce the best ROI (Return on Investment).

How businesses use predictive analytics

Businesses use predictive analytics in a number of ways, such as the one discussed above.  In addition, a number of tools, such as CRM (Customer Relationship Management) use predictive analytics to determine marketing strategies. Another type of predictive analytic is CLV (Customer Lifetime Value) which uses purchase information to classify customers into groups and determine the level of profit reflected by each group, which is used to build marketing strategies to each group.

Descriptive models and predictive analytics

Descriptive models are often overlooked as tools for generating predictive analytics because they suggest strategies that will generate better results without being able to quantify how much better the results will be.

An example is the TRA (Theory of Reasoned Action).  This model states that buying behavior is impacted by a consumers attitudes and beliefs about the products, as well as the norms related to that purchase.  This theory, of course, underpins how social media works. Social media helps build attitudes toward products based on the most credible sources — our friends — and establishes norms of behavior when we see all our friends buying the product.

So, why aren’t these descriptive models used more frequently in businesses.  In part, that’s due to poor exchange between businesses and academics who seem to speak different languages.

Predictive analytics and social media

Marketing in general, and social media marketing in particular, are not heavily influenced by predictive analytics.  Although, that’s changing as supercomputers allow organizations to use massive data captured during transactions to build predictive models of what consumers buy and factors that impact their purchases.

Still, relatively few companies use predictive analytics to drive marketing strategy.  Sometimes, when I pitch to prospective clients, I’m shocked at how few demand any true analytics from their agencies and almost none even understand the concept of predictive analytics.  If the agency provides any analytics, it’s commonly simple ones such as # of Fans, # or RT, or other somewhat meaningless data.

Agencies and in-house marketing employees often develop simple correlations as a way to build social media marketing strategy.  For instance, they might notice that certain types of content drive more engagement or that posting at certain times generates more engagement, so they do more of this.  But, this lacks to depth of understanding necessary to build predictive analytics.

I have several proprietary predictive analytics tools I use to help clients optimize their ROI.  For instance, I have a complex algorithm (model) to help businesses generate leads for the sales force from their email marketing programs.  In other cases, I build predictive models from scratch or use descriptive models to generate predictive analytics, such as my hierarchy of effects in social media.

Hausman and Associates

Hausman and Associates publishes Hausman Marketing Letter and the monthly email newsletter of the same name.  We also provide cost-effective marketing and social media through our virtual agency concept.  We welcome new clients and would happily provide a proposal to show you how we can make your marketing SIZZLE with predictive analytics.

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Marketing Analytics with SPSS

I spent the morning at a workshop hosted by SPSS (an IBM company) — thanks a bunch for the invitation.  Marketing analytics sure have changed in the last 20 years since I started using them — sure there’s still regression and logit and discriminant, but there are lots of new tools, too.

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Image: graur razvan ionut /

Today, I’ll give you an overview of the cool new modules and features of SPSS and, over the next couple of days, I’ll provide more details for each one.

Through development or acquisition, SPSS has a full line of tools for predictive analysis from data planning and data collection through analysis, reporting, and deployment of solutions.  The diversity of tools and ability to buy modules individually allows customizable solutions for a variety of business and non-profit problems.

A valuable feature of all the SPSS software is that the various modules fit together seamlessly and share a similar interface, making it easy to move between modules and reducing the learning curve.  The products also interface with other software products such as Excel.

Today’s session on predictive analytics dealt primarily with data collection, text analysis, new features of SPSS version 19, data mining, and Cognos Business Intelligence software.

Data Collection

This program, actually a group of related products, simplifies the process of designing visually appealing surveys and deploy them using wizards.  Surveys can be deployed through various media, including online and mail, and can be translated into various languages.

Text Analysis

Competing with more established products like nVivo and Hyperresearch, SPSS has developed a text analysis program that uses native language to search a variety of textual materials to find patterns in word use.  The categories can be collapsed into thematic patterns.  These categories can then be imported into SPSS Statistics and are coded as dummy variables.

SPSS New Features

SPSS is getting ready to release version 19, so they presented new features of the software.  Primarily, improvements have been made to make analysis run faster.  Another improvement is the ability to add text block to graphs removing the necessity of exporting graphs to a word processing program to add interpretations or other descriptions to the graphs.

Other changes have been made to make sharing data and output easier and allow for some modification of analysis by other users with only a browser.

Data Mining

Modeling software allows users to analyze vast amounts of data searching for unexpected patterns hidden within the data.  At lunch, someone asked how this differs from the basic SPSS data analysis and the answer is that it makes no assumptions regarding the relationships between variables while basic analysis requires at least a superficial understanding of the relationship, for instance, whether its linear.

Modeling software can handle text data in the way discussed above.  It can also export data to a server when more power is needed to analyze large data sets.


IBM recently acquired Cognos, which provides business intelligence (BI) to track a firm’s Key Performance Indicators (KPIs).  Cognos software provides highly customizable marketing dashboards to increase understanding of the data and displaying it graphically.

Additional Tools

In addition, SPSS offers new modules to handle direct marketing, which allows customized offers to customers based on past behavior, as well as calculates Customer Lifetime Value (CLV), which is important for grouping customers and determining how to market to them.  There are new tools to handle missing values and other data preparation tools to remove outliers, etc.  SPSS has also acquired AMOS, one of the top software packages for Structural Equation Modeling (what used to be called Causal Modeling) used for simultaneously evaluating constructs and relationships between constructs.

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