Target population: Young women aged 17-28 who already buy from the brand or are part of its core customer demographic. Medium-high income, sufficient purchasing power for the brand. Values fashion and image, active on social media.
Because almost all young people who are active on social media
use Facebook, we used Facebook ads manager to find out the size of our
population and fed it into a sample isze calculator to figure out how
many responses we need. First we made a custom target audience on
Facebook Ads manager that matched our target population. Ads manager
automatically calculates how many Facebook users fall into that
population, which was 4.2 million in our case.
Then, we fed our population size into a sample size calculator while specifying a 90% confidence level and 5% error margin. Our sample size ended up being 270, which is doable in a week for a fashion label.
Our strategy is designed using the SMART goal methodology. We specifically chose to concentrate on a particular user type. We created a user profile for a calculated buyer of a high-income bracket. When we concentrate on creating a user whom may be an outlier we will be able to design a strategy for more. We can create a brand that is more desired, and attractive to those who are easier to target. The motivation of the strategy will be to save money and become part of a campaign that is funny, current, and part of the user, Jen’s set of daily actions. Receiving 20% off for a product you plan to purchase is a good incentive. Hoping to reach a sample size of approximately 270 is realistic, especially when using the Facebook ad campaign that our user faces daily. Fortunately, tracking will be easy to do within a week and results can easily be gathered for a budget of $100.
Sample profile: Jen, 23, single young professional living in southern california, upper middle class, active on Facebook, Instagram, Pinterest, shops online often, has an active nightlife, fan of lightweight, neutral colors.
Strategy (note: we use Kate Spade as our hypothetical fashion label just so that we can pick a uniform look and feel. All of these were made from scratch)
We aim to engage our customers through multiple channels, namely in-store, ecommerce, and through social media.
We value the opinion of our existing customers, because driving return purchases is the best to increase sales without the cost of acquiring new customers. Our in-store strategy targets women who already shop at our brand. On every receipt, we’ll include a link to the survey and the sale associate will point out that if the customer completes the survey, they will receive a discount on their next purchase. This incentive not only has zero cost but also drives sales in itself, because customers are inclined to use the discount and even with it the store still profits.
The same strategy can be applied to the online store. The homepage will have a callout that directs the customer to take the survey for a 20% discount off their next purchase. More effectively, we will also have a trigger on the cart page telling them that they can take 20% off their purchase immediately if they fill out the survey. Since the customer is already about to to buy at full price, it is very likely they will spend the extra 5 minutes to knock off 20%. This is a common strategy many online retailers use to convince customers to sign up for loyalty programs.
In the aforementioned channels, the survey will be marketed as “We’re letting you decide our next big product”, because customers like it when their voice matters. The following is an example of a callout that will be placed on the homepage of the online store:
Of course, no modern brand can overlook social media as as a channel to engage potential customers. The key to successful social media campaigns is different from that of successful in-store and e-commerce campaigns. While in-store and e-commerce campaigns are mostly incentive driven, it’s more important for a social media campaign to be fun, exciting, and curiosity provoking. To achieve that, we’re going to promote the survey on Facebook and Instagram through posts that are titled “Lit or Sh*t?” We will also translate the survey into a Buzzfeed quiz of the same name. Rather than using a conjoint analysis method of breaking these questions down into comparable tasks, we are using Max Difference task, to prioritize performance attributes. Max differences uses ‘Most Likely’ or ‘Less Likely’ comparison model, this is most often seen in surveys like “hot or not,” or the swipe left/swipe right model on Tinder. Allowing for a liking system providing some more direct feedback.
Lastly, here’s a GIF for Twitter and Tumblr: