Five Questions on Client Engagement – An Interview with edynamic Head of Strategy, Daniel Huss

Author: Meghan Lockwood | Categories: Customer Engagement, Digital Marketing, Marketing Automation, Digital Analytics

From strategists to software firms, it seems like all the smart marketing money is moving into customer engagement. According to FastCasual, 93% of companies plan to maintain or increase their marketing budgets on engagement this year. Similarly, Inc. Magazine reported that ongoing, meaningful contact is the key to actually driving revenue today.

But what really matters to marketers in the field? To more about how to approach engagement in your firm, we sat down with Daniel Huss, edynamic’s Director of Strategy at our last webinar, to cover some questions that are troubling marketers trying to implement engagement strategies today.

Question 1: What mistakes do people make in defining engagement today?

When we think about the dictionary definition of engagement, you would talk about a formal arrangement to get married, or an arrangement to go somewhere or do something at a fixed time…

That doesn’t really encapsulate what it means to measure what is appropriate and important to your business on the web. Each business is unique, so it’s also nearly impossible to define engagement in any kind of standardized way, and that’s why I kind of would like to shy away from that word.

I think we’ve got stuck in this pattern where we’ll get a communication, “ Hey, we need to be measuring engagement,” or “We need to increase engagement.” But, because of the ill definition that surrounds, it we just don’t know what engagement is, or how to make it useful.

That’s large because people forget to add context. You need to understand the context around your business and what’s important and how the way the users want — and need — to interact with me online.

Question 2: Is there a way to see what’s sending my website visitors away?

I think bounce rate is a pretty solid metric for that.

But again, if we talk about turning your visitors away, we need to know where they are landing on your site. So, you don’t want to look at landing pages alone, but you need to segment by landing page, and then look at your bounce rate segmented by landing page or section.

If one visitor lands on one individual part of the site and the bounce rate is 70%, and another user lands on a different part of the site the bounce rate is, say 40%, what are differences between those?

Before you can parse out those differences, you need to know what you expect the performance of the page to look like. What was the goal of the page? If you have an average overall site rate, segmented your traffic, it based on landing pages – then you can compare that average bounce rate to each other,  comparing one landing page to another to see what’s more effective.

Question 3: What are some of the tools to measure social media analytics?

In the context of social media, I like to use three different metrics.

The first metric is amplification rate. That is simply the measure of share-per-post. Which means, if I’ve posted twice and I’ve had a twenty shares total, then I’m getting an amplification rate of 10 or shares on average per post. You then want to take that data and contextualize it into an average, so you can tease out whether or not one post is performing better than another.

The next social metric is applause rate. This is similar to the amplification rate, but instead of looking at shares, the applause rate focuses on likes (or favorites in the case of Twitter). The applause rate looks at: How many likes per posts are we getting?

Finally, you want to think about conversation rate. How many comments, how many direct messages are we getting per posts that we put up?

If you are interested in tracking those rates, there’s a great tool out there, called trusocialmetrics.com, which I think is free up to a certain amount. It covers a huge variety of social channels and it will automatically calculate that data for you.

Question 4: Can marketers link marketing automation with their analytics platforms to get consolidated customer insight?

Well… that answer is a resounding “Yes!”

I think it might be easiest to walk through an example. Let’s think about a practice area in a law firm. If an email communication goes out, without integration, they may only be able to figure out the open rates of that email communication. But, what if they want to see how many clicked through to their website?

If you want to integrate your systems, it comes down to technology. Modern content management systems like Sitecore or Oracle are all going to aggregating that data for you. But, if you don’t have that budget, you can also look into tools like Omniture and Google Analytics to set up that integration.

For example in Google analytics there’s a thing called the URL builder where you’re able to append UTM data to the end of the link where you can specify campaign that that email is a part of, the medium that it is (email) and source. So when someone clicks on that link, it will transfer that information based on that URL back to your Google analytics account.

Of course, the advanced marketing platforms should also connect to Salesforce or your CRM into that data, so that we can see your conversion rates. You can start to do some very powerful things once you know conversion rates that are coming from a website, such as putting dollar values to your clicks and forms.

Question 5: Is it possible to predict prospect behavior on the basis of historical analytics and, if yes, how effective you really think that is?

Big data is one of those large words that goes around. I still think that companies are struggling with small data, but yes it is possible to start getting predictive data. But, even if we’re still struggling with pulling simple metrics together, predictive analytics is definitely where industries are headed.

And, at the very basic level, if you’re able to look at visits to your websites you can pull a lot of interesting data. For example, from year to year you can potentially track a weekly cycle of data, where perhaps you’re seeing less traffic on the weekends than on Wednesday or Thursday.

I would recommend that at this stage you’re likely going to get 80% of the knowledge that you need from your web analytic package, which will enough to make decisions based on.

Other ways you can pull in predictive data via are partners like ComScore or Rapleaf who are able to connect a user’s online profile with other information about them. Using a tool like that, you’re able to understand the user in a deeper way, and can potentially predict things about them.

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