We work in an increasingly complex programmatic world with viewability, cookie deletion, cross-device attribution and third-party data-enriched DSPs to deal with. So when you introduce a DMP to a marketer, they either shrug at the need of adding another layer to ad-tech, or just call it out as the last thing on that shopping list order of priority.
As a rule of thumb, for every dollar a marketer wants to spend, they are paying about 25-35 cents in being able to deliver that message. That’s creative services, DSP charges, attribution, ad verification, reporting all included. Add to that dynamic creative, a DMP, attribution — and the cost goes up. And add to that the pain of managing separate contracts.
So while the industry is nowhere near consolidating around that problem, the campaign efficiencies you can drive by using your own data for retargeting, audience extension and look-alike modeling-based prospecting can deliver incremental ROI gains that pay up for the entire stack.
As a marketer, you are running campaigns to create buzz using attitudinal data. After creating that buzz, communicating product features based off behavioural data. Then following it up with retargeting to convert, and for the converts, execute cross sell/up sell campaigns.
At each stage, you can use your own data to target, retarget, and extend audiences. You are sitting on a gold mine of the right first-party data or second-party data (someone else’s first-party data).
You can also buy data to fill in the gaps.
A segment strategy requires you to understand the source of the data (online signals or offline actions), the method of collection (first-party, second-party or third-party), the process of creating a segment (Boolean rules based or algorithmic look-alike) and understanding what the resulting segment attribute is (descriptive or predictive).
Some segmentation strategies applicable to campaigns include:
Targeting exclusions If someone who was targeted based off attitudinal data using targeting data baked in into a DSP or third-party data in a DSP, and has seen a certain creative, then apply exclusions based on exposures or engagements with creative. Easy to start with, and paves the way for including more signals in that exclusion beyond just post-impression campaign data, to add post-click data. This is where you will see the most impactful gains in your campaigns, and finally stop wasting money on advertising to the same person. This also means you can run control /test campaigns based off attitudinal data using a research company to validate your digital campaigns and their potential buzz/lift. I am referring to the Millward Brown type assisted campaigns.
CRM-based targeting You have the ‘what’ signals (what are users doing on your digital properties) and the ‘who’ signals (CRM data containing age, gender, NBO, churn potential, loyalty status, product owned) that you now combine to create a segment that you can activate across programmatic display. You can even add a layer of geographical targeting, or in the mobile space geo-fencing data to make your targeting more specific to messaging based off location.
Frequency capping When building a segment, you can apply recency- and frequency-based filters. For example, a segment of men looking at a creative for Product A, exposed to a banner three times in the last seven days. The advantage here is while DSPs offer frequency capping across a campaign, you can apply frequency capping across multiple DSPs, and even include channels beyond paid channels (paid display, on-site personalisation and email)
Cross sell Once you have a combined segment of behavioural (clickstream, products bought) and descriptive (lifestyle, education level, NBO, NPS Score) data, you will see segments qualifying for next best offer available for you to target at the right stage. This could be an output by the advanced analytics team that uses product ownership and then applies market basket analysis to find associations between product ownership with high confidence and lift. This segment could now be targeted by executing a campaign via the DMP on your owned channels (site /app/email) or a subtle targeting off-site. This also means that you can provide quants applying advanced statistical techniques on all offline data — a faster way to go to market in targeting those segments. This also gives them a way to learn quickly from the success of their targeting strategies and make changes to the recommendations coming in. In simple words, the traditional market basket analysis now has a faster go to market on digital channels with a faster learning cycle, and the additional ability to add more data points beyond just product ownership when executing on models delivering NBO.
Retargeting This is pretty straightforward, especially if you are a Travel and Hospitality, Retail or FMCG play, where you might have experienced this yourself where you searched for a sector, a destination or a hotel property only to see those ads follow you on the internet. But rather than just doing a simple retargeting based off the current search, you can apply a layer of signals you have on that person to change your retargeting strategy to have segment-based retargeting where offers could change by loyalty status, for instance. Rather than a simple “because you searched for this sector here is a message reminding you to book it” it’s adding a layer of past purchases.
Web behavioural targeting This leans towards building segments combining either data that’s coming in from a third-party data provider which is tied to behavioural or predictive signals, or it’s based on signals from your own clickstream. The resulting segment could either be descriptive or predictive depending on campaign strategies that you want to use to target individuals based on what they have done in the past, or what the intention and hence the future behaviour would be (in market automobile enthusiast because they looked at car reviews).
Dynamic creative Dynamic creative is a topic in itself, with some simple offerings that allow users to upload a file containing parameters you want to change (Fare, Sector, Location, Price) and create multiple creatives at a low cost (which is the main advantage here), to others which allow for real-time decisioning to render a creative based off data coming in and a segment qualifying for some pre-determined logic. The ability to make mid-campaign changes, and change some parameters mid-flight, is what differentiates a true dynamic creative solution from a pretender.
The combination of a data-management platform powering segments that are tied to templates in a dynamic creative solution, and also providing the real-time data that is driving that decisioning rather than the need for separate retargeting tags, allows for the DMP to deliver incremental value. For instance, although the person falls in a retargeting segment based on the on-site behaviour, they are also then helping test and learn from thousands of creatives. These are creatives that are not just changing sector and price but perhaps copy, creative image, button colour etc, and also serving as inbound data source for the DMP to qualify or disqualify segments after having been exposed to certain creatives, which paves the way for sequential messaging.
Sequential messaging As the name suggests, the idea here is to target a segment by using the ad impression data as an inbound data source to change the targeting message after a certain number of ad exposures. This helps with campaign efficiencies by attempting to avoid campaign fatigue and taking the prospect through a sequence of messages which could be changes based off number of exposures and more importantly, across devices. And these can be changed mid-campaign.
A DMP-powered campaign strategy can bring in efficiencies as programmatic ad spend increases, and has the ability to help marketers focus on driving loyalty and customer lifetime value. 2016 is the year the marketing stack and the ad tech stack comes together, and the DMP sits right in the middle of your marketing strategy making that possible.
So understand campaign strategies, understand the use of descriptive versus predictive signals, empower your traditional quants, and get set for never-before- seen campaign efficiencies
About the Author
Aman Singh (See below) is a senior Solution consultant with Adobe which is which is a corporate member of the Which-50 Digital Intelligence Unit. Members contribute their expertise and insights to Which-50 for the benefit of our senior executive audience. Membership fees apply.