the social metrics

Using Customer, Affiliate and Influencer Data to Geo-Target Your Marketing

Word of mouth and a strong referral network is becoming increasingly important. This has followed the growth of social media, review sites and more connected consumers who can often influence others to buy a product more so than an ad or product offer. 

With that in mind, I’ve been mashing together data about our customers, affiliates and influencers by US state. 

The idea is to model out a potential ‘referrer network’.

That means understanding where we have a good customer base that can refer others to our products. Or a strong affiliate network that resells our products. Or where influential businesses, like bookkeepers, might recommend new customers to our products.

With a bit of modeling, data on these groups can be mashed together to identify where we have the greatest potential for word-of-mouth or affiliate marketing. 

It also highlights locations where we don’t have this support base and, therefore, need to invest more in marketing activities. 

The point of all this is to look at more advanced ways that we can target our marketing spend across the US and be more efficient with how and where we market our products. 

The idea of the referrer network model helps us understand where we already have customers, affiliates and influential professionals recommending our products for us. This means that we can target locations where our referrer network is not as strong and spend marketing dollars on under-served areas. 

I’m still in the early days on this type of mash-up model but I’m starting to identify clear geographical gaps in our referrer network. I’ve mapped out the mash-up using Tableau. States are color-coded to understand where we don’t have a strong referrer network and potentially need to invest in targeted marketing efforts. 

Please note: This is just example data for illustration purposes only. It is not actual company data.

What do you think about this approach?

Some Ideas on Measuring The Quality of Collaboration →

My latest article over at The Community Manager offers up some ideas and tidbits for community managers looking to measure the quality of collaboration. 

TheCommunityManager.com (TCM) has some really great advice for any current or would-be community managers, written by others who run some of the best communities on the internet. If you’re interested in online communities, you should definitely check out TCM and their Twitter chat with the hashtag #cmgrchat.

Measuring The Value Of A Community Manager →

My article over at The Community Manager, discussing some metrics to track when looking at the value of a community manager’s impact for a brand.

Social Advertising Meets Search Engine Data →

(Click headline link to view full story)

And there we have it - social advertising is now combined with Google search engine data to help advertisers target Facebook users based on their search queries.

This new service was announced on Business Insider and is offered by Chango, a service that collects data on some 300+ million search profiles. 

This is a huge step up for companies advertising on Facebook because it offers much more purchase-based, real-time data targeting based on what people are searching for in Google.

This is huge on a couple of levels:

1. The service is connecting social network data with search-based data. The opportunity for learning across social networks AND search is is very impressive.
2. This is the first time (that I know of) where Google is really about to be challenged by a competitor using data from search. 
3. Imagine using search data alongside social network data for highly specific targeting and (even more) granular marketing tactics. Amazing. 

Of course, there might be trust issues and privacy concerns given the level of scrutiny on Facebook’s data protection recently. But all of those discussions are yet to come.

For now, this opens up a lot of potential for companies advertising on Facebook.  

Measuring Information, Not Just Content

A colleague sent me a great blog by Sirius which said that customers want information, not content. 

The main point of the blog article was that readers often have to wade through buckets of content to get the actual information they’re after. 

So, thinking about measurement, I wondered how you could measure whether your information was getting through. 

I’ve been talking about the rise of content marketing and that researchers need to think about how they’re measuring their organization’s content marketing. But the information angle is a good reminder that metrics shouldn’t be restricted to views or shares. 

Content measurement and metrics should also focus on how much information is getting through to the reader. Particularly in industries where the right information can make or break a sale (e.g. B2B data storage hardware and software).

To do that, you could look at metrics around things like:

• Length of time on articles
• How many people are going to support from an article because the information wasn’t clear or raised more questions
• Bounce rates on content pages
• How other departments (e.g. service or sales) are using (or not using) the information pieces
• Who is engaging the articles (e.g existing customers looking for help)
• Ease of digesting the information

These are just a few ideas but a reminder to not only think about content sharing, but to also think about whether the intended information is getting through to your readers.