Setting-up a mobile campaign for airline passengers (Part IV)

Following the recent news that we've launched easyJetText, a white-label version of StreamThru for easyJet passengers, this is the fourth in a series of postings looking at how advertisers (including airport retailers) can target passengers in the terminal building at the time when they're most receptive.

Earlier posts discussed:

- Defining your offer
- Selecting your target audience and measuring the response rate to your campaign
- Refining your campaign to increase conversion rates

In these previous posts, we saw how you can analyse redemption data from your campaigns to determine the best routes, best days of the week, and best times of the day to target your offer. This approach works well for generic offers (e.g. 10% off all purchases, £5 off when you spend over £30), but for more specific offers you can achieve higher redemption rates if you the more advanced targeting options discussed below.

Note: StreamThru and its white-label airline variants, allow a maximum of 2 offers to be sent to each opted-in passenger. So, where there is competition from advertisers to communicate with these passengers, StreamThru favours those offers that are most targeted (on the basis that a more targeted offer is likely to be a more relevant offer). So, it pays to spend some time refining your campaigns.

FINE GRAINED TARGETING
One way to improve the effectiveness of your offer is to go beyond targeting based on time and route by introducing gender, reason for travel (business, leisure, or visiting friends and family), and postcode (e.g. selecting those postcodes that underpin your target geo-demographic segments).

To measure the effectiveness of this type of targeting, we suggest you initially run your campaign with no targeting for a short period (e.g. 1-2 weeks) to capture some baseline performance data.

Once you have this baseline data, you can apply your preferred targeting and run the campaign for a similar period. By comparing the redemption rate to your baseline data, you can see how effectively your targeting has influenced your campaign. Then, by making small refinements to this targeting, you can optimise campaign performance.

Note: When collecting baseline data, you may want to limit the number of offers you send, to control your budget. StreamThru enables you to do this.

SPLIT TESTING
Another way to improve campaign performance is to use some form of split testing.

For example, to test if gender influences redemptions for a particular offer, you could start by setting up 2 promotional codes on your POS system. Then, within StreamThru you could set up one variant of your offer for males and one for females, allocating a promotional code to each. By comparing redemption data for the 2 promotional codes, you'll quickly see if gender is a factor in the campaign's performance, and if so, you can tune the targeting of your offer accordingly.

SERIALISATION
If your POS system supports serialised promotional codes, you can gain much deeper insight into who is redeeming your offers.

To set this up with StreamThru, you first need to allocate a range of promotional codes within your POS system that are either unique within a day or unique within the life of the offer. Next, tell StreamThru about this range of codes, so when we send out your offer we can include a unique offer code for each recipient.

Note: Since each promotional code is tied to an individual passenger, you only need to collect this promotional code and the date and time of the transaction at the point-of-sale to completely close the redemption loop.

With StreamThru, we provide blind-access to passengers, so we never reveal who the specific individuals are who receive your offer ... but with serialisation, you can now analyse redemptions by gender, reason for travel, and postcode (to tie back to your geo-demographic segments), as well as by flight, day of week, and time of day.

With serialisation, the quickest way to refine the targeting of your campaign is to run an offer for a short period of time with no targeting, and then analyse the redemption data to determine the types of passenger who are most receptive to your offer (e.g. male business travellers flying to Rome, Milan, or Madrid from Monday to Thursday). Use this information to target your offer for maximum effectiveness.

NEXT STEPS
In the final posting in this series, I'll look at how you can set-up limited availability offers and how you can fulfil non-retail offers. In the meantime, if you're interested in advertising with us, please email us to request more details.