We now have multiple ways to push deals to shoppers in a store by leveraging beacons. Multiple zones can be set up to push notifications to a shopper who breaks the "virtual cone" for a department such as diapers, fish, wine etc.
Web site content management systems have had a suite of tools for years to enable companies to send thousands, if not millions of emails to promote products and convert prospects to buyers. But new leaders in InBound Marketing such as HubSpot now advocate not only targeted email, but personalized email campaigns:
If personalization is now the best practice for web sites, what are forward thinkers suggesting is the best practice for push notifications?
PlayHaven suggests "there are simple ways to make the most of push notifications":
“Gone are the days of a 'spray and pray' push notification engagement strategy.
Your players have unique characteristics that differentiate them from one another. If you've tested any engagement campaign, you know that what resonates for some players can fall completely flat for others.
This is why targeting defined segments with targeted messages is more important than ever. Treat your players like individuals - send them messages that are relevant for them…”
So, personalization is important. So is relevance and context. Maybe we should investigate one of the best personalization marketers. That would be Netflix.
How does Netflix do personalization?
Let's zoom out and see what we can learn from Netflix, where 75% of viewer activity is driven by recommendations. In a Wired article, The Science Behind the Netflix Algorithms that Decide What You'll Watch Next, we can slide down the learning curve from what they have learned. Consider these statements:
- "If you liked 1960s Star Trek, the first non-Trek title that Netflix is likely to suggest to you is the original Mission: Impossible series (the one with the cool Lalo Schifrin soundtrack). Streaming the latest Doctor Who is likely to net you the supernatural TV drama Being Human (the UK version). Watch From Dusk Till Dawn and 300 and say hello to a new row on your homepage: Visually Striking Violent Action & Adventure.
- By looking at the metadata, you can find all kinds of similarities between shows. Were they created at roughly the same time? Do they tend to get the same ratings? You can also look at user behavior—browsing, playing, searching. Sometimes what’s similar depends on who you’re talking about.
- Testing has shown that the predicted ratings aren’t actually super-useful, while what you’re actually playing is. We’re going from focusing exclusively on ratings and rating predictions to depending on a more complex ecosystem of algorithms.
- We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. All that data is fed into several algorithms, each optimized for a different purpose. In a broad sense, most of our algorithms are based on the assumption that similar viewing patterns represent similar user tastes. We can use the behavior of similar users to infer your preferences.
- People rate movies like Schindler’s List high, as opposed to one of the silly comedies I watch, like Hot Tub Time Machine. If you give users recommendations that are all four- or five-star videos, that doesn’t mean they’ll actually want to watch that video on a Wednesday night after a long day at work. Viewing behavior is the most important data we have.
- A lot of people tell us they often watch foreign movies or documentaries. But in practice, that doesn’t happen very much."
Conversion rate lift with personalization in e-commerce
If personalization is to become the Holy Grail for brick and mortar retailers, there must be a conversion rate lift to justify the investment to deliver personalization. There isn't enough data about conversion rate uplift in stores yet. But we can learn from e-commerce. Kurt Salmon's insight report titled 1:1 Retailing, The Future of In-Store Retailing said this about the lift achieved on-line:
off online: The conversion rate of nonorganic
traffic delivered to a site without personalization
hovers around only 2% to 3.5%, but give that
customer personalized recommendations based
on their personal history and products he or she is
browsing and watch those conversion rates climb
tenfold, according to comScore."