MarketingExperiments Blog: Research-driven optimization, testing, and marketing ideas

The huge benefit of optimization and testing is to have your customers tell you what is most effective – which headline, which offer, even which value proposition – with their real-world actions during actual purchase decisions.

Of course, for this to work, you must be able to listen to what your customers are telling you through their actions.

In a world where “big data” is a big buzzword, many marketers might take this ability for granted. However, in the MarketingSherpa 2013 Marketing Analytics Benchmark Report, 20% of marketers told us they have very limited or no data …

Q: How much analytics data does your organization collect?

A full 40% of marketers only have “an average amount of data,” which does not sound like an overwhelming vote of confidence they have the information they need to intelligently plan, and execute, tests that will help them learn more about their customers.

As you review your analytics capabilities and plan for future improvements, Andrew Wise, VP of Global Sales, Prospectvision, offered a series of questions to help guide your efforts:

• How many discrete marketing tools are you using?
• How many channels (and sub-channels) are you juggling?
• How are you reconciling the data you get back from multiple activities?
• Do you know which campaigns are working? For which segments of your database?
• Can you watch a prospect move through stages of sales-readiness? Can you map your marketing actions to those stages?
• Can your sales team see what you see? In real time?

To the list, I would add one last crucial question …

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You’ve finally set up tracking on your site and have gathered weeks of information. You are now staring at your data saying, “Now what?”

Objectively interpreting your data can be extremely overwhelming and very difficult to do correctly … but it is essential.

The only thing worse than having no insights is having incorrect insights. The latter can be extremely costly to your business.

Use these six simple steps to help you effectively and correctly interpret your data.

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It’s so easy to latch on to numbers. 50% increase. 20% decrease. However, without meaning — not just labels like “bounce rate” or “page views,” I’m talking about real understanding of their impact on your bottom line — those numbers are pretty worthless.

So how can you use metrics and Web analytics to improve your marketing performance?

Dr. Flint McGlaughlin, Managing Director and CEO, MECLABS, will share insights from our online marketing lab on today’s free Web clinic, at 4 p.m. EDT, “Metrics Simplified: How to translate your Web analytics into ROI.”

But first, we asked your peers how they use metrics. Here are a few of the most helpful responses:

Take a step back and think about ROI

My metric tip:

1. Go outside to a café. Leave your notebook/tablet behind. Take a piece of paper and a pencil.

2. Order a decent coffee and remember to tip.

3. Ask yourself (and be honest) – what is really important to me and my business? There can be 1,000 metrics out there that you can calculate.

4. Narrow down to your top three metrics, and then go discuss this with your team.

All metrics are good, but not all metrics are relevant to ROI. Find out what is relevant to ROI, what will give you the intelligence to do a better job, and then track them.

NOTE: Have a way to record them for later.

Oh, also – you can always experiment with different metrics as you go along. The main thing is to have less than four; you don’t really need more than that.

– Vijay Vasu, Founder, GunShot Digital

 

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So you want to optimize the amount of success you have converting your customers?  Well, one approach could be to optimize the customers with which you choose to do business. In other words, only market to customers who can really get value from your product.

Not only can you gain more business, but you can also find customers who are more compatible with your organization. This allows for smoother transactions with a higher success rate, which in turn raises the profit with fewer headaches.

How do you do that? It comes down to some math, namely statistics. If you have a data analyst on your team or in your company, I’m going to show you one tactic they can use to help you choose customers to market to who are much more likely to choose your product.

This analysis can even help you set pricing. After all, customers who can get more value from your product will likely pay more for it as well, or, at the very least, need less incentive to encourage them to buy.

Partition analysis helps you predict who will buy

Partition (or decision) trees are a multivariable statistical approach to identifying and classifying members of a population into groups based on a set of dichotomous attributes that are unique to them. The first step, just copy and paste that sentence into an email to your data analyst to show them that you know what you’re talking about.

All the above sentence really means is that you can use these advance statistical approaches to separate the wheat from the chaff of potential customers.

One of the benefits of this type of multivariate analysis is that on top of classifying groups, they can be used to predict which group a particular individual member of the population will be in. If your current and potential customers make up the population, this method will tell you if the potential client you seek to do business with will be a great fit for your company, or if they will be more hassle then they are worth.

In other words, you’re looking for potential customers who have attributes similar to those who have already bought from you. It’s the marketing equivalent of your buddy asking you if your football-loving girlfriend has any sisters.

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If you’ve been reading this blog for a while, then, chances are, you’re probably testing. That’s good …

… at least some of the time.

Marketers who aren’t testing may actually be better off than the ones that are

If you’re not careful, you could be running tests that tell you one thing when, in fact, the situation is completely different. You could be making critical decisions based on bad data. And these are the worst decisions you could make, because you’ve got the data to confirm that you’re right, when you’re actually doing things incorrectly.

This is why we were so surprised when MarketingSherpa’s Landing Page Optimization Benchmark Report came out with the following chart in it:

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There are so many difficult decisions to make in marketing

- Which headline will perform the best?
- Which value proposition resonates most with my potential customers?
- Which call-to-action will be most effective?

This is why evidence-based marketing resonates so strongly with some marketers. As opposed to taking a random guess to answer one of these questions, why not make the decision based on prevailing evidence?

And yet, this raises another challenge. To make good evidence-based decisions, you need accurate evidence.

To help you make business decisions on a solid footing, in today’s Web clinic at 4 p.m. EDT (educational funding provided by HubSpot) – Bad Data: The 3 validity threats that make your tests look conclusive (when they are deeply flawed) – we’ll show you a few of the ways we ensure our marketing tests, and the data they produce, are valid.

But first, we asked your peers for their top marketing data quality tips. They covered a wide spectrum of marketing data approaches and use cases… Read more…