Database Marketing Today

Building Better Web Communities and Social Media Programs using Persona Segmentation

2010-03-05T14:08:00.000-08:00

In integrated marketing, we use past purchases and descriptive data to target prospects to move them to action. On the web, prospects already know what they want and are actively seeking solutions for their needs and they are willing to interact with us if we provide them with faster, more exact solutions. As marketers creating web communities and social media programs, we can use this willingness to interact to better understand what is driving visitors and to create communities and social media sites that provide them with the information, tools, and products they need. Given that we’re talking about social media, it may seem unnecessary to say this, but…I really should: The best way to find out what people want or what motivates them is to ask. Survey them. Here are some tips on what to ask, how to build, and how to use persona segments to improve your web, social and direct marketing programs.

Think Beyond RFM & P for Web & Social Marketing

To best define your web and social media prospects and customers, you need to better understand five dimensions of their personality which determine the nature & extent of their social interactions. They are:

• Drivers - the strength and sources of motivation that move you to action. Drivers are critical in determining the amount of effort an individual will use to address their needs and the way they interact with a web community (and the frequency of those interactions). What we want to discover about an individual’s motivational drivers are (1) are they internally or externally driven to action, (2) what is the source and (3) how strong is their drive.

Survey hint: Most people cannot tell you their drivers. Ask a series of opinion questions they can quickly respond to with a strongly agree to a strongly disagree [called a 5 or 7 level Likert scale question]. Example: “I eat out mainly for the speed and convenience” “The most important part of dining out is being with friends and family”. Because you want them to simply react to each statement, ask 20 to 30 questions - one after another. Then use factor analysis to determine the underlying drivers.

• Triggers – life and special events, occasions or stimuli that force, entice or encourage an individual to action. In most companies, trigger events or triggers combined with drivers are useful in determining what communities to build on your website or in your social media programs.

Survey hint: When asking trigger questions, give them specific questions about where they go for expertise and information. Include competitor sites, blogs, TV shows, social sites, magazines, etc. and be sure to give them ample space for “other”. The other comments are often key insights into how individuals search for solutions.

• Search Strategies – the media, channels, and tools we use to find the expertise to address our needs. In understanding search strategies, focus on all the media they might use to get expert opinions and information about your products and services. Ask questions about the media sources they use and then “drill down” on each one to learn the specific magazines, TV, websites, blogs, social, and other sources of expertise they like to use.

• The Purchase Experience – the process and methods we use to make a purchase – from initial impulse to final purchase and use. We have found it extremely useful to focus on the time from start of search to the purchase decision, where and how the purchase is made, and the frequency and amount of purchases in recent years. In developing the survey, we focus on understanding their last purchase as we have found it most relevant to predicting future actions.

• Household/Business Situation – information describing the individual and his/her household. If you develop your surveys using database customers or prospects or a national panel, you can overlay this data and avoid asking these ‘personal’ questions.

Survey hint: If you do ask about their income, family composition, etc…keep it short and relevant. We generally never ask more than 5 demographic or lifestyle questions. And we try to avoid them if possible in our surveys.

Executing the Survey

Once your web survey is ready, consider surveying web and social visitors and database customers. Also, you might consider building a national panel and surveying its members. Web and social media visitors will give you the insights of the active searcher, your database customers will provide past purchase behaviors and valuation measures to the analysis and the national panel can show you markets you may be missing with your current marketing strategies.

Once the data is collected, your analytical team will develop the persona segments. A persona segment is a group of individuals with similar triggers, needs, and drive elements. Use the survey data and database purchase history to describe and value each segment. You are looking to see similarities and differences between the persona segments and understand their lifetime value.

Building Better Web Communities and Social Media Programs using Persona Segmentation

Look for the “Persona Gaps” – Each persona segment has different information, product, and support needs. Examine your current web and social sites and evaluate their “fit” with the needs of your highest value persona segments. Where there are “gaps”, develop the information they need, invest in creating special tools to find the “best fit” products quickly, and tailor customer service to acknowledge the trigger event and respond accordingly. It is the “gaps” between your sites and their needs that drives visitors to competitors’ sites.

Create Communities for the Highest Value Persona Segments – Start with your highest value persona segment as a pilot project and create a special community site for these prospects. Examine their profile and determine their needs and wants which become the foundation for your persona-based web community. Add blogs, articles, special tools, special access to customer support, and even forums to promote interactions between the persona visitors and you. Then adjust your Search Engine Optimization (SEO), Pay per Click (PPC), and banner marketing programs to take likely persona members directly to their community site.

Develop Touch Points to Gather Persona Information and Take Visitors to the Right Community – Most visitors are anonymous but, if you ask them, they will allow you to “classify them” if you give them something special in return. Review the driver, trigger, and social responses for each of your persona segments and develop 3 to 5 questions to obtain the information you need to classify them into the persona segments. Add these questions to your prospect profile pages and make them required to access the tools and information you created for each persona community. Visitors will answer the questions and give you their email addresses if they see a real and immediate benefit. When they give you the information, take them immediately to the best community to address their needs.

Summary

In summary, knowing the drivers, triggers, search strategies, purchase experiences, and household situation gives you important knowledge to improve and better target your web and social media programs. Ask your web visitors about themselves and let them guide you to greater market share and improved profits.

Randy Hlavac is the CEO of Marketing Synergy, a web and database analytics firm in Naperville, IL.

Are you Mashable?

2010-01-06T12:22:00.000-08:00

If you are a marketer looking to get into social media, you need to become a frequent visitor to Mashable.com. This site is an effective 'how to' on how to develop twitter, facebook, and other social sites into sources of strategic advantage to your company. For example, go to the home page and click on their facebook or twitter guidebooks. I'll bet you will find useful tips on how to make these two resources more valuable to your company.

In addition to tips, Mashable also has useful and factual articles on how companies are using social media. I find their examples are useful to learn how to make these resources more valuable and actionable for my company and my clients.

If you aren't visiting mashable, do it now. It will be worth you time and effort.

Primer on Statistical Sampling Measures

2009-12-29T12:15:00.000-08:00

In many direct marketing channels [direct mail, telemarketing, email marketing], we control exactly who is offered a product/service. Because we control the targeting of the offer, this means we can scientifically test different offers/communications against samples to see if we can find a better way to target our markets. While many marketers - particularly those new to direct marketing - are often intimidated by statistics and the concepts required to build statistically reliable samples, there are a number of free tools you can use on the internet to assist you with the statistical part...provided you understand how to use them. That is the mission of this blog entry.

There are two key uses of statistics in developing direct marketing samples. The first is to build the correct sample sizes to accurately read the market. This means having samples which allow you to compare your control sample with a test sample to determine if the test is a winner. This calculation is done before you create your marketing plan.

The second application is after your campaign is completed. When done, you might see test panels which have exceeded the control in the critical measures you find important. In this case, you might have a "winning" test package which can now become you new roll-out and control. The key question is "while the test looks like a winner, can I be assured it will perform that way once I roll-out the test?" What you DON'T want is to accept a test panel and then - in the next campaign - roll it out to the majority of your market with disasterous results.

To address these two statistical sampling questions, here are the things you need to know and sites you can visit to find sample size calculators:

Determining Sample Sizes

If you create a sample size that is too low, you run the risk to accepting a test as a winner and then findings it doesn't work when rolle-out to larger populations. If you create a sample size that is too large, you are wasting money [most of the time]. So creating the appropriate sample size is important...and relatively easy. Here is what you need to do:

1. Determine the expected response rate for your campaign

Take a look at past campaigns and determine the average response rate you will receive from the product your are selling and the channel you are using. If you are new to the channel and/or the product, ask your list brokers, lettershops, or processors to help you determine the "average" response rate you will get from the channel and the product. If you are unsure, estimate low.

2. Determine the Acceptable Error for the channel / product
This sounds like a diffult step but it - too - is relatively easy. Take a look at your past campaigns again and see the "above and below average" levels your see for the same channel / product. For example, if the average response rate is 1% with a high level of 1.2% and a low of .8 percent, we would say our average response rate is 1% plus or minus .2%. The .2% is the acceptable error. If you don't know it, estimate it low. Be sure to not be too skewed by seasonality. What you want to determine is how accurately can you predict the response rate around an acceptable level of error.

3. Establish your Confidence Level
This is relatively easy. How much error can you live with? If you are a direct marketer, not much. Generally, all statistical sampling calculations are done with a 95%, 98% or 99% confidence interval. This means if we see a winner, we want to be right in reading this result 99% of the time. When in doubt, use 99%.

Once you have these calculations, then you need to determine your sampling size. You can do this by entering the numbers into a sample size calculator. Where can you find them? Here are two free calculators which work well:

Here is one from Torque.com
Here is another one from Open Up to Mail

For example, if you had an average response rate of 1% with an acceptable error at .2% and a confidence level of 99%, the sample sizes for our control and test panels would need to be at or larger than 16,474. To make life easier, we should probably test 16,500 or 17,000 in our test and control panels. Going higher NEVER gets you into trouble. LOWER...BIG TROUBLE!

Both calculators work well. If you can't get these to work or need more info, contact me at info@msinetwork.com

Identifying the Winners

You now have a test campaign which might be a winner. It has a stronger response rate compared to the control panel...but your not sure. How can you determine if it truely is a winner?

The answer to this is another statistical calculation and - like the last one - there are calculators which can make your work simpler.

When you sample, you are accepting some level of error around an "average" performance rate. What does this mean? Remember the bell curve?

If you have a control package with a response rate of 1%, if you mailed the same size population over and over again, the response rate would not always be 1% but many would be slightly above 1% and others slightly below 1%. Some would be significantly different [outlyers] but most would cluster around 1%. When we set a confidence interval of 99%, this means we want to consider all of the possibilities around an average response rate of 1% except for the extreme .5% at the high and low extremes of the bell curve. This means we consider 99 out of 100 campaigns.

Why is this important? If we have a control with a response rate of 1% and we get a test "winner" at 1.2%, we need to know if the test will replicate if we accept it as our new roll-out pacakge. In other words, the 1.2% might be a reliable winner or it might be the "high side" of a bell curve which will likely not happen again. How do we determine if it is a reliable winner? With a simple statistical test.

For this test, you - again - need to know 3 things...all of which you can get from your campaign results. They are:

1. What is the size of your samples?
This is what you calculated in the last step. Let's use the example sample size of 16,474.

2. What is the response rate?
Here we have two. Our control is 1% and our test is 1.2%

3. What is the confidence level?
Again, let's be consistent and use 99% ... just like our sample size calculation.

If you use a result range calcuator, you get the following results:

Control 1% plus or minus .2%
This means a high of 1.2% and a low of .8%

Test 1.2% plus of minus .21%
This means a high of 1.41% and a low of .99%

What does this mean? If you look at the high and low ranges, you will see that the low end of the bell curve of the test result [.99%] overlaps significantly with the high end of the control [1.2%]. This means there is considerable risk in accepting the test as the new control.

What do we ideally want? We want results where the high of the control does not touch the low of the test. This means the test is clearly better and we can roll it out with confidence.

To calculate these types of measures, you can use a results range calculator like that offered by Torque. To see one, click here

I hope this gives you some insights into using basic statistical measures. Once again, if you are having problems, please email me at
info@msinetwork.com or respond to this blog.

While this explaination will not be copmlete for a statistician or a sophisticated direct marketer, it will get you started in developing statistically reliable tests. Good luck and let me know if you have questions.