Tag: digital marketing

The Evolution of Online Display and the Importance of Programmatic Buying

Ryan Metcalfe on

Display_Ads

What is Display Advertising?

Display advertising is described by Miller, M. (2010) as “banner ads in all forms”, including video format, standard banners, brand awareness banners, interstitial banners and interactive expandables. Each can be used to promote different messages from multiple touch points along the consumer journey. But the evolution from when the ad space was just bought and sold between advertisers and publishers has been huge. Technology has completely digitised the business into a much more efficient purchasing process.

The ad exchange was created to give an opportunity for advertisers to buy and sell specific audiences rather than unspecific packaged impressions by the thousands (which the ad network previously offered, Shiffman, D. 2008). Sellers would make their audiences available on their platform; buyers would then pick their audiences and then bid on them. As technology improved and this method of ad purchasing became more apparent, both buy and sell sides realised that they could make the process more efficient. The below graphic shows how the industry is now set up in layman terms:

simple ad buying set up

A problem arose when it came to companies competing in these real time bidding auctions. Some firms didn’t have the technology or knowledge to carry out these transactions. It was vital that ad space was being bought correctly and on sites that wouldn’t damage the brand. To manage this process, Demand Side Platforms (DSPs) were created, where constant optimisations used data to create more efficient purchasing decisions for the advertisers (Busch, O. 2014). Of course the publishers had the same technological and knowledge limitations so Sell Side Platforms (SSPs) were created to optimise the selling points for these different publishers.

So now we go onto look at how programmatic buying has changed the way media is purchased through the ad exchange.

 

What is programmatic buying?

Put simply, programmatic buying is the use of automated technology to faster and more efficiently trade digital media (Smith, M. 2014). Real time bidding (RTB) was just the start of programmatic buying. It has now evolved into much more than purely auctions on the Ad Exchange, with advertisers and agencies now being able to use programmatic techniques to transact, not only unreserved inventory, but reserved inventory as well (Busch, O. 2014). Programmatic meant that marketers could bid to show a different add to a different user based on data collected about that user. This data is collected from cookies that are placed in the text information when data from a web page is downloaded to the users computer when they click on the site. Improving this data pool to refine the programmatic process has made it the ideal tool for online marketing, giving advertisers the ability to serve the right ads to the right people at the right time, what all marketing should strive for. On top of this audience segmentation targeting, it has allowed digital marketers to set budgeting parameters like what they are willing to pay for an impression, creating another efficiency this time in monetary value.

Programmatic has had a huge growth rate by a 70-80% uptake as marketers begin to embrace new formats for online display (Yuan, S. et al, 2013). Once there are more engaging formats to choose from, there becomes more opportunity for the industry to become smarter about their marketing and develop a consumer journey through display. From a branding perspective this higher impact inventory hasn’t been there until recently, until these ad formats came along there’s been more of a direct response focus using standard units which are not very engaging. However, mixing these branding formats with RTB is where a problem starts to creep in.

 

What issue does programmatic buying create?

The technology and data that is the backbone of programmatic tends to be obsessed over too much, losing focus on what is really going to drive click through rates and engagement from the user (Busch, O. 2014). Ads can become subject to lacking creative ideas as firms can lose focus on attracting the customer.

Programmatic has been more frequently used at the end of a consumers journey because it’s where measurement is more accurate; but with the development in accuracy shown in the blog about attribution modelling, return on investment can be measured more efficiently higher up the consumer journey. This creates a challenge for creative agencies as programmatic reaches into the brand space, it is going to deeply affect the way they develop ideas and profoundly affect the assets they produced. The granularity of targeting that programmatic allows means that there is an increasing quantity of assets that needs to be created to benefit from this targeting. The difficulty for creative agencies is developing these assets to the same quality but just in larger quantities to really maximise the opportunities that this kind of audience targeting generates.

 

To conclude…

Has programmatic buying and real time bidding brought about the end of creativity? It depends on the creative strategy implemented. Media agencies have to work more closely with creative agencies and these creative agencies have to become more agile. If a brand wants a creative unit turned around in 24 hours, it has to become possible to turn it round in this time without losing quality. Creative quality is key. After all, what’s the point of having the right ad in front of the right person; at the right time when the ad looks so bad it doesn’t drive engagement

 

For further information, you may find the following of interest:

Define It – What Is Programmatic Buying?

A super accessible beginner’s guide to programmatic buying and RTB

10 Things You Need to Know Now About Programmatic Buying

 

References

Busch, O (2014). Programmatic Advertising: The Successful Transformation to Automated, Data-Driven Marketing in Real-Time. 2nd ed. Switzerland: Springer International Publishing. p75.

Miller, M. (2010). Display Advertising. In: The Ultimate Web Marketing Guide. USA: Pearson Education.

Shiffman, D (2008). The Age of Engage: Reinventing Marketing for Today’s Connected, Collaborative, and Hyperinteractive Culture. California: Hunt Street Press. p177.

Smith, M (2014). Targeted: How Technology is Revolutionizing Advertising and the Way Companies Reach Consumers. New York: American Management Association.

Yuan, S. Et al. (2013). Real-time bidding for online advertising: measurement and analysis. Data Mining for Online Advertising. 13 (3)

Using Attribution Modelling to Generate More Efficient Digital Marketing

Ryan Metcalfe on

process-youth-buying2

What is Attribution Modelling and why do we need it?

Aimlessly spending marketing budget on digital marketing can be very costly to a business. Fifield, P (2008) admits that although in marketing there will always be leakage in spending it is important to identify where this is so it can be minimalised. According to Banasiewicz, A. (2013), actions taken on successful tracking and analytics of big data is the key to this. But how do you analyse all this data at your disposal? Attribution modelling!

Attribution modelling is how you attribute value to the different channels that contribute to conversions or sales on your website (Ryan, D. 2014). This can then be used to make better decisions with your digital marketing efforts. The models are the sets of rules which are used to determine how the conversions and sales are attributed to different touch points along the conversion path.

First of all let’s create an example scenario with “company ABC” which illustrates the need for a more rigorous attribution modelling system by outlining the issues with the previously used last click attribution (LCA) model. LCA uses information taken from the piece of marketing that, as the name suggests, was last clicked on before the customer converted (Phillips, J 2014), where a conversion is defined by the company. A conversion can be a newsletter sign up or a complete sale for example. The effectiveness of the marketing is then directly attributed to that last piece of marketing and the complete online customer journey is disregarded and forgotten about. Marketing analysts looking at an LCA model would then look to weight the marketing spending towards this “more effective” channel. Let’s look at the implications of this:

Below are four channels of online marketing that company “ABC”, for example purposes, are using. Then below each of these are the value attributed to them via a last click model based on their conversion, which for ease we will count as a sale.

LCA model sales

From the above it looks like PPC marketing is the most effective means of generating sales. However the consumer’s journey has not been completely considered at all. The method of working out how marketing spend should be assigned should not be as black and white as solely looking sales figures drawn from the last piece of marketing interacted with. Certain marketing methods are used to purely generate consideration for the product or service and thus are unlikely to immediately generate a sale yet are still important to the customer journey (Schönhoff, A 2014).

Below I have created an example of a consumer’s purchasing journey which should more accurately show that there is more to be considered than just the last click a customer makes, and also demonstrate the value of the entire marketing plan as a whole. The boxes are colour coded to reference the channels above that the marketing activity stated is part of.

conversion path

As you can see this user journey has a lot of touch points from a digital marketing perspective and using the LCA model all of this customer’s conversion would be attributed to PPC as it was the last piece of marketing they clicked before converting. A good way to view a customers journey is to use Google think, an excellent tool to understand how certain acquisition channels assist conversions in your industry and how the length of the customer journey impacts the final sale.

The clicks and impressions achieved via display, social media and email marketing must have aided the conversion to some extent so we need a way to analyse that and see how they form part of the sale. We do this by observing our data through multiple attribution models and deciding which works best for the data available. Other models include (Phillips, J. 2014):

  • First click – where the entire value of the sale is attributed to the first piece of marketing in the conversion path
  • Linear – where value is evenly attributed across every element of digital marketing in the conversion path
  • Time decay – where more value is given to the most recent piece of marketing to the final conversion and consecutively less to the rest of them with the least being the piece of marketing furthest from the conversion
  • Position based – where value is attributed based on position of the marketing. For example more value is attributed to the first and last interaction in the conversion path than those in the middle

However, the point is not to choose the best model and stick with it, but rather to use all of the models to gain an insight into the conversion path and then efficiently weight digital marketing spend based off of these decisions.

Are there downsides to Attribution Modelling?

Attribution modelling can’t answer all of our digital marketing needs exactly and generate the perfect model of where digital marketing budget should be spent. Kaushik, A. (2013) says that “A lot of that [problems with attribution modelling] is because of all the stuff we don’t know. There is lots of missing data. And as if that were not enough, there is lots of unknowable data”. This suggests that there is only a certain level of efficiency in this current digital climate that can be reached.

On top of this, firms rarely put all of their money into digital marketing. Offline marketing can’t (yet) be attributed to individuals and linked back to their online activity. This allows potential for other marketing channels to impact the online journey of the user and thus skew results of their online consumer path.

Conclusion

In conclusion, a successful attribution modelling system can help a company generate a more refined digital marketing strategy, aiming toward a maximised return on investment across all digital marketing channels. A lack of information may make it impossible to generate a perfect model that tells a firm precisely how their money should be spent, but using a window with multiple models allows for a more efficient budget allocation. This shows us that no single model is better than the sum of them all combined.

References:

Banasiewicz, A (2013). Marketing Database Analytics: Transforming Data for Competitive Advantage. New York: Routledge. p3.

Fifield, P (2008). Marketing Strategy Masterclass: Making Marketing Strategy Happen. Hungary: Elsevier. p203.

Kaushik, A. (2013). Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models. Available: http://www.kaushik.net/avinash/multi-channel-attribution-modeling-good-bad-ugly-models/. Last accessed 12th April 2016.

Phillips, J (2014). Building a Digital Analytics Organization: Create Value by Integrating Analytical Processes, Technology, and People into Business Operations. New Jersey: Pearson. p157.

Ryan, D (2014). Understanding Digital Marketing: Marketing Strategies for Engaging the Digital Generation. London: Jellyfish. p77.

Schönhoff, A (2014). Does Multi-stage Marketing Pay?: Creating Competitive Advantages Through Multi-stage Marketing. Berlin: Springer Gabler. p25.