Personalization Pitfall #4: Poor Historical View of the Customer

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Poor Historical View of the Customer

Watch this video to learn more about this subject from Brian Monahan, former CMO of Walmart.com 

Lifecycle marketing is a highly engaging way companies can re-activate or re-engage old customers. Using past interaction and transactions online, companies surface relevant products and promotions through different channels to influence a purchase. Sounds simple enough right? On the contrary having a 360 degree view of your customers over a long period of time and in real-time is very tricky for most businesses and our pitfall number 4.

Out with the old…

An old approach to this strategy has been to remarket to customers based on each item they browsed without taking their historical behavior into consideration. If a customer is browsing patio chairs, hammocks, and outdoor umbrellas, they are probably looking to furnish their backyard. Offering them 5 options of patio chairs might not be the best way to influence a sale.




Overcome Amnesia of Your Customers

Your product recommendation engine has to be smart enough to suggest “next best products” or “complete-the-look products” or a product in the same category or brand. Only personalized, smart product placement and recommendations can work to win back customers in the highly competitive market of today.

The key to re-marketing the right way is to connect every piece of user behavior and past purchase in real-time with a deep knowledge of the company’s catalog. Using a holistic customer view, marketers can provide a hyper-personalized story relevant to each user’s context.

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To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.

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  • Receive an audit of your current triggered activities with a marketing consultant

BLueshift solves the message overload problem for marketers. Monitor the frequency of all of your messages across all channels to avoid annoying your customers

Personalization Pitfall 3: Customer Message Overload

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Overcoming the Message Overload Pitfall

Watch this video to learn more about this subject from Brian Monahan, former CMO of Walmart.com 

When you build out your company’s personalized marketing landscape you soon find your volume of messages increasing exponentially. As you set up re-engagement campaigns along the customer journey, the volume of messages across all channels can quickly add up to 10 or 15 different messages. Of course, that doesn’t mean you send them all 10 of these messages in one day or even a week. Customers feel overwhelmed if their inbox is flooded with one particular company emailing them again and again. Message overload is a sure way of ending up in your customer’s spam folder or worse, unsubscribing from all your communications. This rapid deluge of communications to your customers is our pitfall #3.

Don’t Be Annoying…

Message Overload across all channels is a personalization pitfall

Finding the balance between quality and quantity will save marketers from those dreaded mistakes of sending a customer too many messages in a day. But how do you make sure you aren’t sending to many messages across all your channels?

The way to achieve message zen is by smart segmentation of customers who fit a certain criteria based on their attributes and behavior on site. Behavior-based marketing resonates better than single trigger marketing because it tends to be more accurate rather than an in-the-moment action or even sloppy demographic focused bucketing. Grouping together customers who have shown similar behavior and sending a set of targeted messages that are personalized to their persona is a controlled way of using triggers on your site.

Think Beyond the Inbox…

“don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels”

Another way of working around the message overload problem is to build and monitor multi channel campaigns. Marketers constantly compete for inbox space along with numerous other brands. When’s the last time you looked at your inbox and didn’t feel like you were being yelled at by dozens of brands? A quick reminder to complete your purchase and checkout can easily be done via text message or push notification – abandoned cart campaigns are not simply just an email tactic. Dividing your messages across different channels can keep your brand name top of mind and limit annoying your customers. And remember, don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels. Otherwise, you still run the likely risk of annoying your customers with message overload.

Subscribe Now to this series
To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.

  • Each update sent directly to you with extra tips NOT included in the blog posts
  • Access to the VentureBeat Webinar with former head of marketing at Walmart.com
  • Receive an audit of your current triggered activities with a marketing consultant

What is Programmatic CRM? [Infographic]

Programmatic CRM is a technology that enables marketers to be customer-centric and leverage real-time behavioral data to reach every customer on an individual level throughout all of your marketing channels. Bringing Programmatic CRM into your marketing stack enables marketers to finally automate the delivery of consistent and delightful user experiences on every channel with true scalability and greater results.

Reach the perpetually connected consumer across all channels at the moment they are most inclined to engage with you brand.

The building blocks of Programmatic CRM

Programmatic CRM is built up a number of key components that work together to Engage with Segments-of-One:

  • Real-Time Triggers to engage customers based on their actions
  • Cross-Channel Reach to be customer-centric, not channel-centric
  • Personalized Recommendations to tailor recommendations to user behavior
  • Dynamic Audiences for segments that update with every customer interaction
  • Measurability to deliver end-to-end reporting on engagements and conversions


Isn’t it time you stop marketing to stale databases built of attributes and demographics?


Behind-the-Scenes: Real-time segments with Blueshift

(Here is a behind the scenes look at the segmentation engine that powers Programmatic CRM.)

Real-time segmentation matters: Customers expect messages based on their most recent activity. Customers do not want reminders for products they may have already purchased or messages based on transient past behaviors that are no longer relevant.

However, real-time segmentation is hard: it requires processing large amounts of behavioral data quickly. This requires a technology stack that can:

  • Process event & user attributes immediately, as they occur on your website or mobile apps
  • Track 360-degree customer profiles and deal with data fragmentation challenges
  • Scale underlying data stores to process billions of customer actions and support high write and read throughput.
  • Avoid time consuming steps of data modeling that require human curation and slows down on-boarding

Marketers use Blueshift to reach each customer as a segment-of-one, and deliver highly personalized messages across every marketing channel using Blueshift’s Programmatic CRM capabilities. Unlike previous generation CRM platforms, Segments in Blueshift are always fresh and updated in real-time, enabling marketers to respond to the perpetually connected customer in a timely manner. Marketers use the intuitive and easy to use segmentation builder to define their own custom segments by mixing and matching filters across numerous dimensions including: event behavioral data, demographic attributes, predictive scores, lifetime aggregates, catalog interactions, CRM attributes, channel engagement metrics among others.

Segments support complex filter conditions across numerous dimensions

Segments support complex filter conditions across numerous dimensions

Behind the scenes, Blueshift builds a continually changing graph of users and items in the catalog. The edges in the graph come from user’s behavior (or implied behavior), we call this the “Interaction graph”. The “interaction graph” is further enriched by machine-learning models that add predicted edges and scores to the graph (if you liked item X, you may also like item Y) and also expand user attributes through 3rd party data sources (example: given the firstname “John”, with reasonable confidence we can infer gender is male).

Blueshift interaction graph

Blueshift interaction graph

The segment service can run complex queries against the “interaction graph” like: “Female users that viewed ‘Handbags’ over $500 in last 90 days, with lifetime purchases over $1,000 and not using mobile apps recently and having a high churn probability” and return those users within a few seconds to a couple of minutes.

360-degree user profiles

For every user on your site/mobile app, Blueshift creates a user profile that tracks anonymous user behavior and merges it with their logged-in activities across devices. These rich user profiles combine CRM data, aggregate lifetime statistics, catalog-related activity, predictive attributes, campaign & channel activity and website / mobile app activity. The unified user profiles form the basis for segmentation. A segment query matches these 360 degree user profiles against the segment definition to identify the target set of users.

360-degree user profiles

360-degree user profiles in Blueshift

Multiple data stores (no one store to rule them all)
The segmentation engine is powered by several different data stores. A given user action or attribute that hits the event API is replicated across these data stores including: timeseries stores for events, relational database for metadata, in-memory stores for aggregated data & counters, key-value stores for user lookups, as well as a reverse index to search across any event or user attributes quickly. The segmentation engine is tuned for fast retrieval of complex segment definitions compared to a general purpose SQL-style database where joins across tables could take hours to return results. The segmentation engine leverages data across all these data stores to pull the right set of target users that match the segment definition.

Real-time event processing

Website & mobile apps send data to Blueshift’s event APIs via SDKs and tag managers. The events are received by API end-points and written to in-memory queues. The event queues are processed continuously in-order, and updates are made across multiple data stores (as described above). The user profiles and event attributes are updated continuously with respect to the incoming event stream. Campaigns pull the audience data just-in-time for messaging, which result in segments that are continuously updated and always fresh. Marketers do not have to worry about out of date segment definitions and avoid the “list pull hell” with data-warehouse style segmentation.

Dynamic attribute binding

The segmentation engine further simplifies onboarding user or event attributes by removing the need to model (or declare) attribute types ahead of time. The segmentation engine dynamically assesses the type of each new attribute based on sample usage in real-time. For instance, an attribute called “loyalty_points” with a value of “450”, would be interpreted as a number (and show related numeric operators for segmentation), while an attribute like “membership_level” with a value of “gold” would be dynamically interpreted as a string (and show related string comparison operators for segmentation), or an attribute like “redemption_at” with a value like “2016-09-23” will be interpreted as a timestamp (and show relative time operators).

Several Blueshift customers have thousands of CRM & event attributes, and are able to use these attributes without any data modeling or declaring their data upfront, saving them numerous days of implementing data schemas in SQL-based implementations.

The combination of 360-degree user profiles, real-time event processing, multiple specialized data stores and dynamic attribute binding, empowers marketers to create always fresh and continuously updated segments.

Beyond Basics : Advanced Personalization strategies for Re-marketing Triggers

Re-marketing based on behavioral triggers like abandoned cart, abandoned search and abandoned view are must do’s for any digital marketer to engage customers and bring them back to your site or app. More often than not it’s very tempting to do just the basics, may be your e-commerce platform vendor gives few out of box “widgets” to replay the content or products and you can tick a box and call it done. But that would be waste of a great opportunity to engage with your users fully at the moment they are most interested in your offerings and showcase the full depth and breadth of your catalog.

Imagine your self in the shoes of your customer – They were on your site or app for a reason. Why did they abandon their visit? Are they looking for better price? Are they looking for affordable alternatives? Are they looking for quality recommendations? Are they looking for things that go along with their previous purchases? Are they looking for hot new products they heard about? Or are they just window shopping? May be it’s mix or all of them and you can’t tell. But you can tell a better story than replaying what they have seen. Multi-touch campaigns over their preferred channel with content personalized to each user are a great way to showcase and up sell your offerings.

Here are few advanced personalization strategies to try, going beyond replaying the products your customer has viewed or added to cart.

  1. Related Products : “What Other Items Do Customers Buy After Viewing This Item?” Based on the product that was abandoned you should consider including these in your messages. Very often this is helpful as a first touch in moving your customer down the conversion funnel helping them make informed choices.
  2. Up-sell Products : “What are the top sellers in the category of the item viewed”? Based on the category of the product that was abandoned you can showcase your best sellers from those categories and optionally you can restrict it to specific brands.
  3. Trending Products : May be some of your products are not best sellers yet but they have been recently added to your catalog and are already selling out. Consider including them in your messaging with appropriate call outs.
  4. Affordable Alternatives : Your catalog likely has a breadth of products that are in the same category but are more affordable. Add them as alternative recommendations.
  5. Most Discounted Products : Your customers will likely want “quality” products but want them to be affordable getting most value for their money. And may be you are doing seasonal promotions or roll backs. Consider tying them together to the product and category of the abandoned product.

Depending upon your catalog and the number of touch points you have with your customers you can do all of the above or a mix of them. At Blueshift we have built a DIY Personalization Studio to do all of the above and more without needing to go through development or IT cycles. Re-marketing based on behavior triggers is the most effective messaging you can do as a digital marketer. Do not waste a great opportunity to win over the customer by doing just the basics. Learn more about behavioral triggers with our comprehensive e-book. Learn how our clients like UrbanLadder are using advanced personalization strategies to improve their conversion rates by 400%. If you are ready to take your triggered marketing to the next level say hello.

Need for Speed: Why Marketing needs to Adapt to High Velocity Data

This holiday retailing season, as customers shopping preferences shift, will you still be marketing to them using stale data?


With the rise of mobile devices, and the “always-on” user, the amount of time spent on the

internet has nearly tripled over the last 5 years: 450 billion minutes per month in 2010 to more than 1200 billion minutes per month now. The velocity of data is going to continue unabated growing into the future, with some projections pointing to another 10X increase in data velocity by 2020. Interestingly, the increase in the overall amount of time spent on the internet has also translated in users spending more and more time with the same apps or brands.

All of this additional user time is generating behavioral clickstream data for companies at speeds faster than ever before. At large omnichannel retailers, the volume of clickstream data generated in one day now rivals one year of PoS data: or, in other words, there are 300-1000 pieces of unstructured clickstream data for each purchase.

While marketers have long understood the importance of “RFM” (recency, frequency, and monetary value), with the increase in volume of data every day, “recency” has become ever more important. Without near real-time usage of behavioral clickstream data, the value of the data decays, making it meaningless for targeting. For instance, during the holiday season, which is typically the biggest season for retailers, many users are shopping for gifts, and their purchase behavior deviates significantly from the norm. Businesses that can develop processes to understand and react to such data quickly can earn superior engagement and profits.

Despite the rise of big data technologies, most CMOs are increasingly feeling underprepared for “data explosion”. What are the top initiatives that can help CMOs get ready for this new age of high velocity data? Here are our top 3 recommendations:

  • Process streaming data, and store everything: When dealing with low velocity data, you would first model the data to develop a data-warehouse schema; data that’s not modeled would be discarded. With high velocity data, however, you need to complement your data warehouse strategy with schema-less big data infrastructure that can store all data. The idea is to give analysts the ability to play with data to discover insights that can then be modeled. A good example is from Orbitz, which started collecting unstructured data around trip planning from users, and went from 30TB of data storage to 750TB, revolutionizing their hotel sort.
  • Understand “identity” across platforms and channels: Users are increasingly adept at switching between devices and it’s not uncommon for an user to use 3-4 devices, often times in single day, while they shop around. To understand each user, you have to develop infrastructure that ties together seemingly disparate points of data from desktop & mobile into one unified profile. In addition to using well structured identity information like email addresses or customer ID, smart marketers are also looking at fingerprinting technology to fill the gaps in their knowledge.
  • Get machines to help humans with analysis: Once you have the ability to process and store streams of real-time data, the next step is to have your analytics keep pace with the speed of data collection. Purely manual process can impose delays of weeks, and CMOs need to provide machine learning tools to assist their teams of analysts in uncovering insights. For example, creating lookalike audiences with machine learning on real-time data, can help marketers acquire more high value customers. During the holiday season, and other times when user behavior changes significantly, machine learning will always be a step ahead of human modeling. Machine learnt models can then be refined more by humans who can layer in additional business logic.
  • Reduce the time to action with automation: Not only do you need to process data in real-time, you need to be able to act on your analysis faster. This requires a high degree of automation. In the old world of slow moving data, you might have tolerated a 24 hour delay for ETL processes to load the data in your data warehouse, as well as several weeks of delays imposed by manual analytics processes to leverage the data, and batch processes to act on the data.

amazon_emailHowever, in the high velocity world, actions need to be automated to respond to various behaviors, in a personalized manner. Simple automations like browse abandonment emails, or “related products”, can go a long way, as this example from Amazon shows.

The best consumer marketers of tomorrow will be the ones who embrace the challenges of high velocity data. Need for speed, and higher degrees of automation, will become critical capabilities for marketing organizations in this new world.