Customer Data Platform

My main use case for Blueshift  was to deliver recommendations in real time to customers at scale. By using our product catalog and customer behavioral data that was modeled and worked on with the data science team at Blueshift , we were able to create a match recommendation email that was sent out at scale to 70 to 80,000 people a day.

I really enjoy keeping up to date with Blueshift and the AI ways of utilizing AI within the system, especially on the segmentation and result side; it was really quick and really insightful and really helpful.

We were trying to reduce churn, and by utilizing the AI segmentation and insights, we were able to query questions and bring data to the forefront quicker and build segmentation without having to go into the actual segmentation builder. We just built it using the rules and asking that we set up in blocks to ensure that we had trackable data that could give us real insights to A/B test across the data segments we had created.

You were able to utilize and test these things ahead of the market and just be able to feed back and shape the way that things were working. If you saw something that could be iterated on, it was a really nice way to work.

By bringing journeys together, Blueshift has positively impacted my organization. Before, there were lots of channels that were working in silos. By bringing the customer data into one specific place and then utilizing the tools from Blueshift, we have been able to create more of an experience for the customer and not just channel silos.

Blueshift needs to do what they do best and continually look at what customers need, where the gaps are in the market, and just try to deliver a good solution as quickly as they possibly can, working in the areas that are going to have an impact, with AI being one area where they are currently working.

I have been using Blueshift for about four and a half to five years.

Blueshift is stable.

Blueshift's scalability is great.

I rate it an eight just because I think it is a really good tool. It is something I enjoy using; it is really user-friendly. The team is brilliant at explaining, training, or helping you if you get stuck, and there is good onboarding. I do not give it a ten because it is not adopted as much across the UK, so it is a little bit more niche, which is why it is not a ten.

I previously used Klaviyo  before switching to Blueshift because we wanted a CDP, and at that time, Klaviyo  did not offer the recommendation engine that we wanted to go as deep as we wanted to go into it.

We did see a return on investment with Blueshift, but unfortunately, I cannot share that data with you.

My experience with pricing, setup cost, and licensing was initially just okay.

We evaluated other options before choosing Blueshift and looked at Iterable  and Bloomreach; those were the ones that were shortlisted against Blueshift.

My advice for others looking into using Blueshift is to make sure that you know what your business cases are for the product, fully test it out before you buy, ensure that it will deliver what you want, and make sure that you have complex cases that will really get the best out of the product. Blueshift really is a brilliant product, but there is so much you can do with it, and are you going to fully use it? I rate this product an eight overall.

I use Blueshift  to data mine for deep historical information regarding people's interest in certain brands, makes, and models of classic cars. The goal was specifically to record interest expressed in very rare vehicles which would only come up for sale very seldom so that this could be used many years later.

I recorded people's searches, saved searches, saved listings, saved sale listings, and cross-referenced those with imported historical data from previous versions of the site, using similar data of people's saved searches, the searches they had carried out, interest they had expressed in vehicles that were for sale, and bids they had placed on auctions.

Blueshift  has positively impacted my organization by allowing for better marketing. I was able to market vehicles more successfully to potential buyers, which drove better user engagement because people appreciated the personalized nature of the emails they were receiving alerting them to vehicles for sale. Additionally, it increased vehicle sales and sales prices on auctions, along with the number of bids that were received.

Blueshift offers very rich data mining that allowed me to go back far into the history of the site and to cross-match interest in vehicles with similar vehicles. The cross-matching feature helped my business by making it possible to market cars which would be potentially of interest to buyers who had expressed interest in cars of that make and model previously, or who had searched for that make and model previously, or who had searched for similar vehicles which were of a similar make, model, era, or type. For example, if someone was interested in Ferraris, they would also be interested in Maserati or Lamborghini.

Blueshift could be improved with better instructions and better installation. The installation was very difficult to do, and getting it set up to work properly was very problematic and took a very long time. I ended up having to go back and forth with Blueshift team quite a lot for a considerable amount of time, and it took roughly twice as long to install and set up Blueshift as I had been told it would. I actually received a partial refund on what I was paying for it because I wasn't able to use it for the initial period due to how long it took to install. The initial emails that it sent out weren't doing a particularly good job of serving people information that they would prefer—information that would engage them. That was because of the installation being so difficult to get in place. Once it was up and running, it was excellent.

That was really the only issue I ever had with it; the rest was fine.

I used Blueshift for a year and a half.

Once Blueshift was fully installed and working properly, the accuracy and reliability of output was excellent. To start with, it was somewhat unreliable.

I had no issue with governance or security; they were more than adequate for my needs.

I ended up having to go back and forth with Blueshift team quite a lot for a considerable amount of time.

I would recommend that others looking into using Blueshift plan the installation in advance, make sure they are fully familiarized with how it needs to be installed, and allow extra time for the installation over and above what is suggested.

Overall, Blueshift was an excellent product. Once I got past the initial headache of the installation, it did some very clever things which I was very impressed with.

There were a couple of times that it was a little repetitive, asking what I thought should be changed about the product; other than that, it went off very well indeed. I would rate this review a 9 out of 10.

My main use case for Blueshift  is cross-channel customer engagement and lifecycle marketing. Freshworks has a large customer base across multiple products, and the challenge is communicating with different segments in a relevant, personalized way across email and push.

Blueshift  has significantly impacted my organization. The time from insight to campaign used to involve a data request, a wait for the data team, a CSV export, and a manual upload. Now a marketer can identify a segment and have a campaign live the same day.

I can put some numbers to that. Email engagement rates went up about 25% after we switched from batch sends to behavioral triggered campaigns, which is not surprising because the messages are more relevant. Our onboarding completion rate improved by around 22% in the first six months after rebuilding those journeys in Blueshift. We have seen a measurable reduction in early churn in customer cohorts that went through the new onboarding experience versus the old one, somewhere around 15% reduction, which is significant at our scale.

When I open Blueshift, the first thing I do is check campaign health, looking at sends, deliverability, open rates, and click rates across active journeys. If something looks off, I want to catch it early before it compounds. A big chunk of my day is spent on segment building. Blueshift segmentation is really powerful. You can build audiences based on behavioral data, predictive scores, event history, and custom attributes. I am constantly building and refining segments for different campaign needs. That might be identifying customers who have not logged in for 30 days, or customers who used a specific feature and are candidates for an upsell, or customers showing churn signals based on usage drop.

Blueshift's best features, which deliver the most value for me day-to-day, are behavioral triggers. The ability to fire a campaign based on something a customer did or did not do in real time is the core of what makes our lifecycle marketing work.

The biggest frustration or friction point I have hit with Blueshift is the learning curve on journey logic.

There are features I wish Blueshift had that it does not offer today, specifically better native reporting.

If I could change one thing about Blueshift to improve my workflow, it would be the journey testing experience. Right now, testing a complex journey properly requires a lot of manual simulation: setting up test profiles, manually triggering events, and checking outputs. If there was a proper journey simulation mode where you could walk a test customer through a journey and see exactly what they would experience and when, that would dramatically reduce the time we spend on quality assurance before launch and the mistakes that slip through.

I have been familiar with Blueshift for about two years.

Support and the customer success team at Blueshift have been genuinely good, which is something important to know about Blueshift.

Before landing on Blueshift, I was using a combination of tools: a separate email service provider for email, a different tool for push notifications, and some manual segmentation work being done in the data warehouse and handed off. Everything was siloed.

It took about three months to get up and running with Blueshift, connecting our data sources, setting up the profile schema, and getting the first journeys live.

My team needed formal training on Blueshift to some extent. Blueshift is reasonably intuitive for marketers once they understand the core concepts: segments, journeys, and recommendations. The harder thing was getting people comfortable with the AI and predictive features because that requires a different way of thinking about campaigns.

I considered Braze  and Iterable  seriously alongside Blueshift. Braze  is the obvious competitor in this space and has a really strong product with good regard. Blueshift's CDP plus execution combination was the deciding factor, along with the AI recommendations. The ability to do personalized product and content recommendations across channels using machine learning without needing a separate recommendations engine was something we had not seen done as cleanly in the other platforms.

Adoption overall has gone well. The marketing team adopted the campaign and journey features quickly because the user interface is clean and the results were visible early. Blueshift's predictive features took longer to adopt. People needed to see the lift data before they trusted the AI recommendations over their own instincts.

There are no features that came up during implementation that I thought I would use but do not actually rely on much today. No problems exist with the paid media integrations.

My advice to someone considering Blueshift who has a similar workflow to mine is to spend real time on your data model before you start building campaigns. I would rate this review at 9.5 out of 10.