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Top Growth Marketing Trends for 2017

With the new year upon us, marketers scramble to put new plans into action while also identifying where we should place more of our efforts and resources. We ask ourselves where can I get the most “bang for the buck” for our organizations. We are looking to grow our current user base while also retaining more of our hard acquired customers.

Publications and journalists, including Forbes, MarketingLand, and others, have surveyed marketers about what they think the biggest needle movers in marketing are going to be in 2017. Of these surveys and conversations, here are the top 6 Growth Marketing Trends for 2017 backed by industry leading CMO’s who are betting big on them. For Growth Marketers, keeping users engaged will be a driving focus for 2017 as a stronger focus on retention becomes a guiding theme with unified, delightful customer experiences powered by powerful technologies.

1. Big Data (in real time and actionized)

Steve Fund, CMO of Intel said, “data is becoming the currency of marketing, and marketers will now have access to more data than ever…marketers will be able to use data to create more personalized and targeted products, messages, and customer engagements than ever before.”

Number 1 on the list = Big Data. (I know, Big data may seem like “yesterday’s news”, but bear with us) For this article we define big data to include real-time data, customer insight, and big data marketing applications. Big data tops our growth marketing trends because the volume of data coming in from consumers continues to increase year after year (be it from explicitly shared data to tracked behavior data), resulting in a rise in the importance of data scientists and data platforms. Data is the building block, the foundation, of customer identity and behavior that powers machine learning models and various marketing applications. As growth marketers, we’re looking to actionize this data faster and faster. Insights are great, however, we need ways to slice and dice the data manually and automatically to really affect our bottom line. In 2017, we’ll see more of the power of this data being wielded by marketers quickly and more easily than ever to create better experiences for every individual…and to build predictive models.

 

2. Artificial Intelligence & Predictive Analytics (for richer individual experiences)

Karen Walker, senior VP and CMO at Cisco elaborated “The key in 2017 will be transforming and analyzing data in order to derive contextual conclusions about our customers. What are they interested in? What needs can we anticipate? From consideration, to purchase, to renewal–it’s about delivering a richer, meaningful, individualized experience that helps our customers make faster, more educated decisions and deepens customer loyalty.”

AI has become incredibly powerful and accessible with the influx of more data over the years and the rise of more sophisticated technology. 2017 will see AI cement itself in the growth marketing space. The ability of machine learning to predict trends in customers and make complex recommendations across product/content categories has given many retailers the edge over the rest. For media companies, machines will curate the content to include in newsletters, onsite, and in app, transforming generic content feeds into timely, personalized content “magazines”.

To get this next level of understanding, growth marketers use predictive analytics to make sense of the scores of customer data to create actionable workflows and customer journeys that allow for the non-linear customer journeys that growth marketers are trying to tackle with legacy automation.

 

3. Marketing Automation (becomes smarter)

The CMO of Ally, Andrea Riley, said that their attention will be on “understanding, at a customer-level, personas, needs, wants and preferences and delivering them in a meaningful way through the channels they prefer to be communicated in.”

Marketing automation includes CRM, behavioral email marketing, web personalization, multi-channel messaging, triggers, and more. This encompasses a large portion of the marketing landscape and why it’s in our top 3. Seeing from this research on the State of Marketing Automation there is a lot of room for improvement in this area. Marketing automation isn’t going anywhere, instead, it is becoming smarter and flexible to incorporate real time triggers, catalog updates, and predictive analytics that drive users through funnels and increase user retention.

In the chart below, 2 out of 3 marketers state they aren’t even using their marketing automation platform to its potential (intermediate level and below). It’s time to review your use of your marketing automation technology to see if (1) there is any more juice you can get from it and (2) if it’s time to think about a change. A tool that isn’t being used has no value to the organization if it remains “rusting away” in the toolbox. (For the 18% who don’t use any marketing automation, it’s time to step up.)

Image from Scott Brinker as he talks about the changing roles of Marketing Automation

In all transparency, this is where Blueshift’s Programmatic CRM shines the brightest. Our technology enables growth marketers to become more customer-centric and leverage real-time behavioral data to reach every customer on an individual level throughout all marketing channels. Bringing Programmatic CRM into your marketing stack takes you to the highest level of sophistication with your “marketing automation” enabling you to automate the delivery of consistent and delightful user experiences on every channel with true scalability and greater results.

 

4. Content (becomes more personal)

The CMO of Grubhub said that moving forward companies will have to “establish a lasting and meaningful connection with consumers” to stay on top of mind and build loyalty among their customers.

Content has evolved from simple text on a website to richer, more visual content such as videos, slides, infographics, etc. Now we are witnessing the next form of content marketing that involves using personalized marketing technology to deliver the right content to the right person with interactive content on responsive web pages, emails, apps, and more. Often, marketers create mountains of content that quickly gets buried, even when it is still relevant or simply worth-the-read –  the problem has always been getting that content to the right consumer at the right time.

Image from Scott Brinker as he talks about the 4th Wave of Content Marketing

Personalization requires data about the prospect in order to make educated guesses about their interest and feed them relevant content. More and more companies are taking a strategic approach to their content creation and distribution along with closely measuring content marketing ROI to make sure they are moving in the right direction. As Growth marketers, we use content everywhere. Making it more personal through recommendations and affinities creates that delightful user experience that creates loyalty and trust that inhibits churn.

 

5. Mobile Marketing (driving greater engagement and retention)

The CMO of Keds, Emily Culp mentioned that she is “constantly evaluating our UI and ensuring that we are delivering not only a brand rich experience but also one that is streamlined enabling a clear path to purchase.”

Research from last year shows that conversion rates are significantly lower on smartphones. It’s no wonder that we see great potential still to make a big impact with mobile marketing by making the user experience less “SPAMMy” and more conversational, more relative, more personalized. When I say mobile, I mean more than just smartphones. Many devices can be involved in this process so treating it as part of a multi-platform or multichannel strategy is more appropriate. I see mobile as being a complementary channel to the overall marketing strategy. Part of the mantra of “being where your customer is” involves reaching out to customers where they are, rather than waiting for them to come back to you. That means being on their tablet, or phone, or laptop with a consistent message that adds value and delivers a personalized experience.

Bring consumers back to your branded properties (including apps and websites) with rich push notifications that include deep lining into your app, images, and even carousels of suggested products/articles from them to read. And don’t forget about SMS messages. The goal is to drive them to engagement and the desired action. Mobile will keep your users and customers engaged and drive higher retention rates when approached intelligently. Mobile optimized sites are now table-stakes. Designing for a mobile-first experience is now expected.

 

 

6. Social Media Marketing

Obviously, social media marketing still has significant weight when talking to marketers but be mindful of what platforms you choose to play in. Statistics show the reduced popularity of some social networks in some countries, i.e. Twitter and Facebook are in decline or plateauing in many western countries while Snapchat, Instagram and Pinterest are still growing in usage. Being particular about your spend and the ROI you get is more important now than ever as many platforms now require a “pay to play” business model to get the reach needed to have an impact. Your customer experience extends across all channels and must drive greater adoption and retention.

 

Conclusion:

So, how does a Growth Marketer begin to make sense of these trends?

In short – stay customer focused. Remove channel barriers. Remove fragmented data sets. Leverage technology anywhere you can to drive results quicker and at scale.

References:
  1. http://www.forbes.com/sites/jenniferrooney/2016/12/19/heres-what-will-command-cmos-attention-in-2017/#7d0470b59758
  2. http://www.smartinsights.com/managing-digital-marketing/marketing-innovation/digital-marketing-trends-2016-2017/
  3. http://marketingland.com/4th-wave-content-marketing-marketing-apps-84108
Listening to your users: Inferring Affinities and Interests based on actual time spent vs clicks or pageloads

Listening to your users: Inferring Affinities and Interests based on actual time spent vs clicks or pageloads

Personalized recommendations rely on the idea the you know the interests of your audience. In absence of explicit feedback, interests are generally derived from clickstream data: session and event (e.g. click) data. But given that sessions can be short lived (bounce) and clicks can be unintentional, they are unlikely to reflect true interests of your audience if you simply count them.

At Blueshift, we choose to actively follow along the individual’s storyline and extract intelligence from each event to gather insights of the user’s intent and interests, so we can provide better recommendations.

Let’s look at a real user example

In the table below, we see an actual clickstream of events from a user on blueshiftreads.com.

Timestamp Session_id Event Category Book title
12:30:24 session_id1 view Biography & Autobiography > Personal Memoirs Eat Pray Love
12:31:29 session_id1 view Drama > American > General Death of a Salesman
13:48:49 session_id2 view Science > Physics > General Physics of the Impossible
13:49:02 session_id2 view Biography & Autobiography > Personal Memoirs Eat Pray Love
13:49:09 session_id2 view Health & Fitness > Diet & Nutrition > Nutrition The Omnivore’s Dilemma
13:49:19 session_id2 view Health & Fitness > Diet & Nutrition > Nutrition The Omnivore’s Dilemma
13:49:35 session_id2 view Poetry > American > General Leaves of Grass
14:09:47 session_id2 view Poetry > American > General Leaves of Grass
14:10:02 session_id2 add_to_cart Poetry > American > General Leaves of Grass

This specific user interacted during two different sessions, browsing books from different categories. If we try to come up with the top categories for this user, based on total number of sessions, we get:

Rank Category Session count
1 Biography & Autobiography > Personal Memoirs 2
2 Health & Fitness > Diet & Nutrition > Nutrition 1
3 Poetry > American > General 1
4 Science > Physics > General 1

As you can see in the table above, Personal Memoirs is the top category while the three other categories tie to second-place (they have been alphabetically ordered in that case), but other tie-breaking rules can be applied.

Time spent ranking

At Blueshift, we developed algorithms to re-rank these categories according to the time the user actually spent on your products and categories:

Rank Category Time spent
1 Poetry > American > General 1212
2 Biography & Autobiography > Personal Memoirs 72
3 Health & Fitness > Diet & Nutrition > Nutrition 26
4 Science > Physics > General 13

Here, we rank ‘Poetry > American > General’ above the other categories. Note that at the end of the original event stream above, the user actually did add the book from that category to the cart. Even if we would have ignored that event, our time based ranking would have indeed capture a category of interest to this user.

There’s more: decayed time spent

You should be careful not to rely on detailed information from a single user on a single day: if the user indeed bought the book he added to the cart, that might just be an indicator of no longer being interested in that specific category of products. Furthermore, you would want to adapt to changing user interest over time.

That’s why we implemented what we call a decayed time spent algorithm, that combines the time spent by users over a certain period of time (say last week) and that weighs recent time spent as more important to the ranking than time the user spent before (say 14 days ago).

Decayed weighting of recency this way allows recommendations to adapt quickly to shifting user interests when they are shopping during holidays and might be looking for gifts for others as well as themselves.

From user-level signal to site-wide signal

Many product recommendations are related to some site-wide top categories of products, like ‘top viewed’. Using our time based algorithms, we can better rank these top categories. Let’s look at another example from blueshiftreads.com where we show you a part (20-25 to be exact) of the top 25 most popular categories.

Using classical session counting, we obtain the following ranking:

category session count
Juvenile Fiction > People & Places > United States > African American 5358
Juvenile Fiction > Girls & Women 5291
Juvenile Fiction > Family > General 5265
Fiction > Contemporary Women 5215
Fiction > Thrillers > Suspense 4971
Fiction > Mystery & Detective > Women Sleuths 4804

However, when we rerank these categories based on actual time spent by the users, we see that ‘Juvenile Fiction > Girls & Woman’ drops from position 21 (above) to position 23 (below), even though it had 76 user sessions more in the 7 days over which this was calculated. User sessions are no guarantee for actual interest (i.e spending time).

category time spent
Juvenile Fiction > People & Places > United States > African American 102164972
Juvenile Fiction > Family > General 100447985
Fiction > Contemporary Women 98897169
Juvenile Fiction > Girls & Women 98340874
Fiction > Thrillers > Suspense 91140081
Fiction > Mystery & Detective > Women Sleuths 87372604

Furthermore, if we rank the categories using our decayed time spent, we see that ‘Fiction > Contemporary Women’ is actually ranked the highest (21) while it was the lowest (23) in the original list. This indicates that this category received the highest time spend by users in the most recent past.

category time score
Juvenile Fiction > People & Places > United States > African American 28461106.29
Fiction > Contemporary Women 28179308.93
Juvenile Fiction > Girls & Women 28068989.26
Juvenile Fiction > Family > General 27608048.02
Fiction > Thrillers > Suspense 26102829.31
Fiction > Mystery & Detective > Women Sleuths 24597921.38
Ok, why bother?

So why bother re-ranking? Well, most catalogs will exhibit a Long Tail in the distribution of popularity of their content: very few items will be very popular while lots of items will be very unpopular. No matter how you rank the popularity of the top-10 categories (sessions, clicks, time, …) out of a 1000 category catalog, these extremely popular categories will always on top. Just have a look at the top 20 categories from blueshiftreads.com:

blog_post_time_spent_top20

As you can see, the top 5 categories do a lot better than the rest. For most businesses there is a lot of value in promoting content from categories other than these few favorites. Therefore, if you can avoid down-ranking interesting categories for users and do this consistently over your whole catalog, you will be able to recommend products from the appropriate category to the users who care for it. In other words, you will avoid the pitfall of recommending an overly popular yet generic product to your users.

But time spent relates to sessions/clicks anyway?

Yes and no. It is true that more sessions correlate to more time users will spend on categories, but not to the same extent: a session length can range from a second to tens of minutes. Have a look at the next graph below.

What we see is the ranking of the 1000+ categories (on the X-axis) for blueshiftreads.com by popularity (on the Y-axis, logarithmic scale) over 7 days, in terms of 3 different metrics:

  • The blue line represents ranking by session count. It is very smooth because it really ranks all categories just in descending order of session count. This is the standard ranking.
  • The red line represents ranking by time spent by the users. It is equally smooth in the beginning (left) because it ‘agrees’ with the session ranking: as mentioned above, the top popular categories will always be on top. But quite soon, the line becomes spiky: the ranking disagrees with session count, and the spikes indicate that this ranking would reorder the categories in a different way (promoting different categories to the top).
  • The green line is the decayed time spent ranking: the same holds as the time spent ranking. This algorithm also disagrees with session count and would reorder lots of categories in the long tail to promote categories of interest to the user.

blog_post_time_spent_ranking_plot

This re-ranking is exactly what you should do to stop recommending the same popular categories to users that might have indicated (time) interest in other categories.

keep-calm-and-ai-on-with-blueshift2

Practical AI for Growth Marketers

A.I has had a media resurgence in the recent past, thanks to the incessant coverage in every outlet and overblown hype for and against what it all means. Underneath the hyperbole there are real breakthroughs but also many challenges and practical considerations in using these innovations. This post by Crowdflower, a crowdsourcing platform used by many for improving the RoI of A.I projects puts it well when they say “A.I is a pragmatic technology that can be applied to solving today’s problems but you need to understand the limiting beliefs of A.I, and replace myths with truths”.

Growth marketers at B2C organizations specifically face formidable challenges in using A.I or machine learning in their day to day efforts. Data at their disposal spans many sources, updating via real time streams and likely runs into petabytes in size. Here are few practical considerations in realizing good RoI from your A.I project investments.

 

Simple vs Diverse data formats:

Today’s customers are tethered to their devices 24/7 and switch between them seamlessly. Advances in Big Data technologies like Hadoop have made it easy to capture raw data in diverse formats and store them across several different data stores usually called data lakes spanning SQL systems, NoSQL systems, flat files and excel sheets. As a growth marketer this is the raw gold mine you are working with and you should prioritize data capture in any format over shoehorning it to a particular data store or schema. A.I tools that you invest in should adapt to this mix of structured and unstructured data.

 

Real Time vs Batch mode:

The half life of consumer intent is getting shorter with each passing year, and customers expect “on-demand” experiences that are contextually relevant and personalized to them across every device. Growth marketers should prioritize simpler AI algorithms and processes that can adapt well to real time data than more complex batch mode solutions that may need several hours or days to execute. Pay close attention to training time it takes to build and deploy A.I models and how fast can they incorporate new data.

 

Complete vs Sparse data:

While it’s ideal to have every attribute and preference known about all users, in reality you will end up with incomplete or partially known data fields despite your best efforts. B2C growth marketers in particular should expect this from day one and invest in tools and solutions that adapt well to incomplete data. Take for example a user location, there may be a mix of user given location data, with device lat/long, ip to geo, inferences from content viewed or searches done and more. As a growth marketer you should prefer A.I tools that can adapt well to the mix of all this data and output best effort answers for widest user base than on few users with complete and clean data.

 

Size of training data:

Most A.I algorithms expect training data to be fed to them and the size and availability of training data is big obstacle to overcome to use them effectively. Certain class of A.I algorithms like Boosted Random Forests are better at adapting to the size of training data than Convolutional Neural Networks aka Deep Learning. Growth marketers should prefer those algorithms that can work with limited training data and have in-built sampling techniques to deal with disproportionate class sizes.

 

Black box vs Explainable Models:

A.I algorithms come in many forms, from easy to understand decision trees to black box complex ones like Deep Boltzman machines. Navigating the black boxes can be tricky, what works today cannot be said of tomorrow and need very careful tuning to yield short term results. Growth marketers should prefer AI algorithms that explain their outputs, and helps marketer understand various factors and weights given to them in realizing that output. Tools that iterate quickly and incorporate domain specific knowledge much more easily are likely to work better in the long term than hyper optimized black boxes with enticing short term yields.

When it comes to the nitty gritty of it all remember that A.I is no magic bullet but a practical tool to achieving your custom goals.

Keep calm and A.I on.