What is the best way to measure paid social success? | DMA

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What is the best way to measure paid social success?


You can only achieve paid media success if you know what success looks like.

If you go into a campaign with a clear idea of your objectives, this will inform how you measure that success.

If you’re looking to generate leads, and convert these leads into sales, you’ll need to analyse the performance of your marketing channels to make informed decisions on the most efficient strategies and where it’s best to spend your budget.

By making ‘business impact’ the driver to identify what paid media success looks like and setting KPIs at the outset, you can ensure that success will always link back to achieving your business objectives.

Avoiding the common pitfalls

One of the main challenges in measuring success comes when you’re running sophisticated multi-channel campaigns.

How do you attribute business value to each of those channels and determine which is providing the most value?

The ‘last click’ model of analysis is commonly used to analyse the success of paid media. This looks at converted leads and establishes where the ‘last click’ from a sale came from.

For example, if a customer first clicked on a paid social advert which did not result in a conversion, then went directly to the site, which similarly failed to convert, before finally clicking on a display advert which did result in a conversion, under a ‘last click’ method of analysis the display advert would be 100% attributed to the sale.

But that’s neglecting the influence that the initial paid social ad did have.

The last click approach makes analysis relatively easy, but also gives inaccurate results. This is not helpful when you’re trying to decide where to focus your paid media strategy. The primary reason the ‘last click’ model is inaccurate is because, as we know, consumers are influenced by a whole host of marketing channels.

Large companies can run up to forty different channels simultaneously, which means the majority of converted leads will have been exposed to multiple channels along the way to their purchase (which is what we call the conversion path).

How can brands use attribution models to look at things differently?

At Mighty Social we look at how to attribute converted leads more realistically.

One way to do this is by building conversion paths with Multichannel Grouping using Google Analytics and assign rules to establish your attribution model.

So, if a consumer only had one interaction before conversion, then the last click could rightly be given 100% attribution.

However, if a consumer had multiple interactions and followed a ‘conversion path’, then the first click may receive 50% attribution, assuming the conversion occurs in the next 28 days, whilst further interactions with other marketing channels would receive a lesser attribution percentage.

This way, a more accurate picture of attribution can be painted, allowing for better informed marketing and budgeting decisions.

It might be helpful to think of this in terms of winning a race in a high performance car. There are so many factors that can be attributed the success of coming in first: position, the car mechanics, driver skill, the weather and the latest tyre technology. How much of the success is attributed to each of the different factors at play will vary depending on the circumstances.

Although setting rules is undoubtedly an improvement on the ‘last click’ model, the model is only as good as the rules you set.

So, what is the best way to formulate an accurate attribution model for digital marketing?

The key to a better attribution model is using a data-driven approach.

By feeding the data we collect (i.e which marketing channels our customer was exposed to) into algorithms, we can actually analyse each conversion path, identify the most effective channels, as well as the relationships between different channels too.

After clicking on a social media ad first, what is the most common method of conversion?

By allowing an algorithm to crunch the numbers, these types of questions can be answered, giving you valuable insights into the inter-connectivity and efficiency of your paid media campaign.

For each individual conversion path an algorithm can attribute accurate values to each marketing channel based upon the wealth of data it has been fed from previous conversion paths.

Clever stuff right?

Working with attribution models means you can identify paid media success with a far greater degree of accuracy than the conventional ‘last click’ model.

We can see how our marketing channels are working for us with these attribution values, and make decisions on budget and resources accordingly.

The result of this? A more effective, and efficient, marketing strategy.

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