How do we incorporate data analytics into user engagement strategies? Interview with Joe Cothrel, Chief Community Officer, Lithium Technologies Chicago, Illinois

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So we've had a fairly broad understanding of what sort of data is available to us as

social media strategists.

We can access it, maybe visualize it, and interrogate it to some degree.

But how do we make logical sense out of it?

How do we integrate social media data into an effective social media campaign?

I spoke with Joe Cothrel from Lithium, one of the leading global social marketing and

customer service analytics providers.

He cleared up a few issues surrounding how to read the data, what data is useful,

and how data can sit alongside effective community management.

If you could take me through what it is that Lithium does and

your role within the organization.

We're a software company.

We have created a platform for organizations to help create and

manage communities of members, customers, partners, etc.

How does data analytics become involved in your role?

There's so much data around how people interact and what people are interacting

about and whether people's interactions are successful, etc.

Which if you think about it, we actually don't have the same data for

geographical communities or local communities in general.

And so it creates lots of opportunities for

understanding whether communities work well.

Whether they're productive for users, whether people are successful in using

them, and whether they're accomplishing their goals.

We kind of distinguish between data, metrics, and analytics.

So when we think about data and communities,

really there are two kinds of data that we think about.

Quantitative data.

That's data about the kind of things people are doing online.

Looking, posting, rating, literally any feature that you allow a user

to use in an online community creates data, or should create data in the system

that helps you track whether they're doing it or not.

But on the qualitative side, of course,

a lot of what's happening in communities is around content.

Conversations, content that uses your posting,

video, images, even sometimes sharing applications.

All of that content, too, is data in the system.

And all that has insights that it can share too.

So we always have to think about, when we think about data in the system,

that the quantitative and the qualitative part,

both the numbers that come out of the system and then the insights.

They are sometimes in the form of words or

opinions or topics that people are talking about.

So that's kind of where data analytics beings for us, is to understand,

what is the data?

How might I use that data to engage my community of users?

Before you actually start looking at the metrics you kind of have to step back

and say, what actually are my objectives in this system?

Why did I create the community?

What am I trying to accomplish?

And generally, what we find is, there are almost two levels of metrics.

There are what we call operational metrics.

Those are the metrics that have to do with what measures are doing

in this system at any given point in time.

They help you understand first of all, whether people are using the system, how

often they're using the system, what kinds of things they're doing in the system.

But then there are our more results-oriented

metrics that might have to do with why you created the thing in the first place.

In general ,with most communities,

the goals really transcend the platform itself.

Is it fair to say that it's an iterative type of process where you might

get some sort of analytics back?

And then you refine what it is you're doing and

then you use the analytics to then develop your, say, social media strategy or

the purpose of what your community might be there?

I think that's absolutely right.

Your measurement strategy will change over time.

Sometimes it changes because your objectives for the community, your goals,

have changed based on the stage of maturity of your community.

In the first hour, day, week, month, generally,

you're just trying to get the engine running.

So the kinds of metrics that you're measuring are very basic, right?

Are people who are coming to the community registering?

A simple ratio of visits to registered members.

Are people who are registering participating, right?

A simple ratio of members at any given time and posts, for

example, in the system.

Are people getting an answer to their question if it's a forum

discussion, right?

A ratio of initial posts or topics to replies.

And so in the early days you're building that just to understand,

gosh are most people getting an answer?

Are most people who are coming here at least exploring

the process of measuring it?

And in the early days you're simply, again,

trying to get those to a certain level of health, let's just say.

But then once you've got your community healthy and growing,

then you're going to move on to basically say, okay,

where do I want to direct this community for its benefit and for ours?

So this is a really good, I think,

overview of what's happening in the analytics environment at the moment.

Where do you see all of this heading towards within the next five to ten years?

I've been doing this for almost 20 years, since the mid-90s.

And one of the things that's always frustrated me about all these platforms

is, I always joke, and developers always hate when I say this, but I say,

your social platform can answer any question you have.

As long as that question is not, who are my users and what are they saying, right?

[LAUGH] Because you could never tell who my users were because

it was largely pseudonymous.

Because it wasn't linked to a database of users we could validate, right?

I mean online interaction is often you don't know who you're dealing with, right?

So, that's one analytic challenge, is to better understand,

even if you know their identity, you might not know their needs and

their preferences and their interests and their expertise.

All of those things you really need to know, I think as a community manager.

But there 's another aspect of that as well, which is the, what are they saying?

And you might say, well, wait a minute, can't you tell what people are saying,

isn't it all public out there?

But it's the big data problem.

Yeah, you can find out what they're saying

if you have a week to spend getting everybody's opinion, right?

But how will the platform helping us understand?

How are they teasing out hot topics?

Things that are exciting people, things that are making people angry, you know.

Valuable content that when people read helps them solve problems or

move on to the next level of proficiency.

And for most people creating communities, we want to be respectful of their privacy.

And we want to create profiles, but allow them to manage those profiles so

that they can control what they reveal to us.

And then on the content side, it's just the text analytics challenge.

It's taking a vast amount of unstructured data and creating meaning from that.

And that's a hard computer science problem.

And anybody who tells you that that's solved [LAUGH] is wrong.

It's a work in progress.

So, those are the two things to me, those are the questions to me, right?

[LAUGH] These are the ones you must answer.

Joe makes some great points about how data can enable us to identify fundamental

issues with online communities, such as how many people are logging on,

what do they click on, and where do they go when they're on the site?

But Joe also notes this may indicate broader concepts.

Like are they happy with the community, if they're successful using the site,

and if the users are accomplishing their goals.

What is of particular interest here is how Joe notes, you can learn to

read your community through the data with more and more experience.

That is, a more experienced community manager will look at the data and

be able to identify particular patterns.

Ultimately, data is a way to continually improve your site or platform.

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