Monogamers, serial monogamers and polygamers: which users make a game successful?
Globally, mobile gaming is a $35 billion market, larger than both the console and PC gaming markets. Despite having existed for less time than traditional gaming categories, mobile gaming is experiencing the fastest growth across the entire gaming sector. The games that capture the majority of the revenues are microtransaction based, meaning they are free to play but include items or features that users can buy in-game.
From past Slice Intelligence research, we know that looking at a user’s purchasing history is a better predictor of online spending than mere demographics. In this paper, rather than making predictions from past purchases on a single franchise, we studied the purchasing behavior of users across mobile games and found predictors of games with high user spend.
Lookalikes looking too alike? How to differentiate models with purchase data
Precision and scale used to be a trade-off marketers had to make when setting up their campaigns. You could either reach tens of millions of people who may or may not be interested in your product, or you could reach thousands of people likely to convert. With advancement in connecting offline data to online footprints and modeling capabilities, marketers have more strategies available to efficiently reach a large scale of targeted consumers. Using their own existing data, they can leverage Facebook’s algorithm to create lookalikes and reach new and relevant audiences.
However, first-party lookalikes are now the norm for marketers, not a differentiator that will impact market share. This is especially true for mobile games where savvy marketers are taking a scientific, data-driven approach to acquire new users with high lifetime value.
Using-first party data is the baseline for these marketers. To gain an edge, they must be effective in leveraging other datasets.
The most common, readily available data for marketers is demographic attributes, but mobile game purchase data analyzed by Slice Intelligence has found demographics do not move the needle.
We applied a logistic regression model using demographic data and we were only able to predict purchase behavior with 64% accuracy. The same model using past purchases as inputs was able to predict purchases with 85% accuracy. What people bought in the past says a lot more about what they will buy in the future.
With platforms such as Facebook offering advertisers to employ multiple data sources and internal algorithms for creating lookalikes, the value of imprecise data at scale has completely diminished. Smaller samples of precise purchase data can significantly improve ability of finding high value customers when amplified by user affinity modeling.
Amazon Echo: Seattle’s sonic boom is felt beyond e-commerce
Amazon has a sleeper hit on its hands with the Echo. Selling this product at its typically low (or no) margin, Amazon has planted a digital home hub, e commerce order taker, personal assistant, digital jukebox, and, yes, speaker, in millions of homes.
What is the story behind the launch? In the following white paper, we will explore these questions:
- How do sales of Echo devices compare with other Amazon hardware devices?
- What do Echo buyers look like?
- How do sales of Echo devices compare against Bluetooth speakers and Smart Hub devices?
- With successful Kindle, Fire and Echo devices, how does Amazon’s device ecosystem compare with Apple’s?
- Have Echo devices’ commerce potential been unlocked?
Jet.com: How will it land?
Jet.com's launch was stratospheric, but what's the story behind the hype? With our panel of 4 million online shoppers, Slice Intelligence can offer a precise, insightful read on what's on Jet's radar; including which headwinds it's facing, and how much runway it has as competition mounts against the biggest ecommerce players.
This white paper answers the following questions:
- How healthy is Jet's business?
- What are people buying on Jet?
- Does Jet's 'Smart Cart' work?
- Will Jet be able to grow sustainably?
- Is Jet stealing business from established online retailers like Amazon and Walmart?
- Friend, frenemy, or enemy of manufacturers and other marketplace sellers?
IoT commerce: an early read on Amazon’s Dash buttons
Technology and trade publications have espoused the importance of the Internet of Things (or IoT) for years, and manifestations of it have hit the mainstream consumer landscape recently in the form of Fitbit fitness trackers and Nest thermostats. Until recently, though, IoT hadn’t affected the consumer packaged goods industry. Amazon, in partnership with major CPG manufacturers has changed that with the introduction of Dash Buttons.
Dash buttons are a new phenomenon, only available to shoppers since March 2015 and not yet been heavily promoted by Amazon. As such, the number of people using the Dash Button is low (less than .1 percent of Slice’s panel bought a Dash Button). This white paper explores the earliest trends of Dash Button adoption and usage.
Retail Disruption: The Rise of Convenience Commerce
We answer the following questions:
- Is Dollar Shave Club's subscription service a model that other retailers should adopt?
- What are people buying with Dash Buttons?
- Have 'Now' services from Amazon and Google put delivery services on steroids?
- How can brands capitalize on convenience commerce shoppers?
Subscription Model Trend and Growth
It’s Prime Time for Need It Now