Must Have Customer Experience Tools

Steve BurgCustomer Success, User Experience0 Comments

jaco must have customer retention tools

Managing digital customer experience can be a daunting task. There is so much that needs to be managed and optimized and you just don’t have the time.

If this sounds like you, you need to think differently about customer experience and start utilizing different tools to help you become more proactive. The customer experience tools below will allow you to constantly gather customer feedback, maintain a single profile or source of data on each customer, and deliver a personalized experience in each channel.

8 Must Have Customer Experience Tools


jaco freshdesk

1. Freshdesk

Freshdesk is an award-winning cloud-hosted help desk solution with useful features and exceptional customer service. Its most significant features are: multi-channel capability; integrated game mechanics to increase agent productivity; multiple SLA policies, smart automations; and self-service portals.

Once set up, Freshdesk turns your support emails into tickets that you can track for rapid and accurate response. It also features live chat and phone support. With the iOS and Android app versions, you can take the help desk wherever you go.


jaco acquia lift

2. Acquia Lift

This site personalization platform can be used to optimize digital interactions between companies and users. Acquia Lift’s features include behavioral targeting, drag-and-drop content targeting, profile merging, segmentation functions and A/B testing.

The syndication hub streamlines data inputs and content management while reducing workload by integrating with any content management software. Acquia Lift also offers easy UI that helps marketers create a meaningful customer experience.


jaco intercom

3. Intercom

Intercom is a fundamentally new way for digital companies to communicate with customers, personally, at scale. It’s a CRM platform with a suite of integrated products for every team including sales, marketing, product, and support. Intercom’s products enable targeted communication with customers on your website, inside your web and mobile apps, and by email.


jaco analytics best customer experience tools

4. Jaco Analytics

Jaco is a new gen usability platform that analytics teams can use to understand how their customers are really using their product. Jaco offers recorded user experiences with your site; no matter how complex the app is or which framework is being used, Jaco can replay every user session as a video.

And you aren’t limited to watching anonymous interactions; you’re also provided a simple API framework to add unique identifiers and attributes to each visitor. Unlike current solutions, Jaco requires zero integration and no programming knowledge to get started. Within 5 minutes of signup, you’ll see real live replays of your users.


jaco qualtrics vocalize

5. Qualtrics Vocalize

This survey software can be used by businesses that need to actively measure how well they meet online expectations by asking their online customers directly. With Qualtrics Vocalize, it’s very easy to obtain customer insights, which can be used to drive strategy and anticipate the customer needs.

It’s very simple to setup and smart enough to fit into your existing systems. Qualtrics also offers great flexibility– you can configure and make desired adjustments in just a few simple clicks.


jaco conversocial software

6. Conversocial Software

This is a complete platform for managing all of a company’s social media interactions. It performs intelligent prioritization of tickets and issues, making sure important ones don’t get overlooked or delayed. It also compiles all of a company’s interactions with each customer onto one screen; simplifying agents’ workflow and allowing them work more efficiently.


jaco ethnio

7. Ethnio

Ethnio offers advanced UX research management. It allows you to send real-time requests to website visitors to give them the option of participating in a study. From there, you can conduct in-person interviews, send them to an online platform, send them to a survey, set up remote UX testing, or ask them to participate in a customer panel. Many people enjoy providing an opinion, and Ethnio helps you leverage that desire for UX research.


jaco userecho

8. UserEcho

UserEcho offers a community forum solution where you can give your customers the platform to ask, suggest, ideate and engage with your sites. Users can vote for features and you have the ability to organize your feedback community through categories and tags.


Five Steps to Transform Your Business in the Digital Age

Steve BurgData0 Comments


Digital transformation is not about technology, rather how organizations wield their digital tools.


Although we are deep in the age of digital, shift we know as digital transformation is far from over. Companies are completely reinventing themselves and constantly adapting to ensure that the next hot startup does not take over their business. However, many companies are buying technologies left and right and then figuring out how to use them just to keep up with the “digital age.” Let’s take a look at five steps you can take to ensure that the technology you are using is actionable.


Be strategic with your investments


Many companies begin their digital journey by making large investments. This approach is called a “boil the ocean,” implying a lack of connectivity to reality and logic. This should be avoided at all cost. When investing, whether in a startup or a partner company, ensure that they are aligned with needs of your company now, as well as your future needs. Test the technology, see if it fits your team needs, and only then make the investment.


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Be business and data driven


The ultimate goal of any transformation is to drive business results right? The entire purpose of digital transformation has to be to increase the value of your company, either by driving increased revenue or becoming a more agile organization. Don’t pay for new data repositories when chances are you’ve already got a treasure trove of data waiting to be analyzed. Keep yourself focused on the end business goal, don’t be enticed by flashy tech that does not drive your bottom line.


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Embrace the power of data and analytics


Data, data, data. This is ultimately what will help inform and drive every decision you make on your transformational journey. Data and analytics are both extremely powerful tools, but also very time-consuming. When you first implement analytics structures, you can find yourself staring at rows and rows of data that seem to make no sense. Stay focused, and find the actionable items in the data. Follow your analytics to determine the next step in your transformation. Let the data be your guide.


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Ensure transformation is company-wide


We may have mentioned agile analytics once or twice on this blog, and there is a reason for that. No true digital transformation can take place without having a system that allows the entire company to become more effective. This is the core goal of agile analytics, to allow everyone access to the information that will let them do their job better. Let the data flow!


Find the balance between people and technology


It is important to recognize that while you may be bringing on new technologies, you are not eliminating your company’s most important asset: human capital. People are seen as the second biggest challenge to successful digital transformation, but are rarely considered a significant contributor to success.


Credit for success is generally attributed to the c-suite and not to the lower level employees. Those who stand in the way of your transformation can be replaced with new employees who view the new processes and flows as standard. It’s harsh, but it is the reality.


These are not steps to be followed in any particular order, but rather an outline of necessary milestones towards digital transformation. The effect these changes will have on the company and its bottom line will be significant, and will only increase from there. Keep these tips in mind, and while the transformation process will not be easy, it will ultimately be worth the journey.


Investing in Analytics Requires More Than Money

Steve BurgUncategorized0 Comments


While budget is necessary to build out your analytics infrastructure, there is a lot more to account for while building a successful organization.


Many large organizations have committed millions of dollars to data and analytics. It is no secret deeper knowledge lays the foundation for successful businesses. Data allows us to stay one step ahead of our competition, to lead our own industry. However, while making an investment in data is a very worthwhile endeavor, the investment alone is not enough.


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According to a yearly study jointly undertaken by Forbes along with Ernst & Young, five areas are deeply impacted by analytics. In our brains, information passes seamlessly from cell to cell, but in the real world, the flow of information is not quite as seamless. We do have to contend with people who hold things close to the vest or are not good communicators. It is how we as leaders deal with this informational flow that ultimately determines how successful our business will be.



Let’s look at the above diagram, taken from the aforementioned study. In the agile analytics model, we would be able to start at any of the steps above and move forward from there. We have analytics at every step and are constantly adjusting to fit those analytics. However, when incorporating the human factor, lag sometimes occurs.


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What is the impact of the human factor?


Since the above study is performed yearly, E&Y have been extremely helpful in providing us the exact answer. Two years ago, only 16% of companies had an enterprise wide analytics strategy. This past year, 23% of companies had made the transition into a model more similar to Agile Analytics. That’s a staggering 43% jump from 2015 to 2016.


So what is stopping 77% of companies from making the jump to agile analytics? Primarily, internal stakeholders: people who don’t believe that they need help from an external source to improve their portion of the business cycle.

What impact has agile analytics had on companies who do make the leap?


This has impacted the companies that have made the transition in numerous ways. In fact, 26% of the companies that took the E&Y survey commented that they had completely changed how they fulfill customer needs and their financing models. This is a staggering change to enterprise level companies. As you can see from the below chart, data is beginning to make every single piece of companies significantly more efficient.



Is the human factor always a barrier to change?


No, not at at. In fact, the human factor is also the leading driver of change within companies. Often times, we meet with the company “champion,” the one person who has taken on agile analytics as their cause and is pushing it to other stakeholders in the company. These are the people who often times become the company leaders due to their tenacity and drive to get more accomplished at a more efficient pace.


How do we overcome the human factor?


This generally comes from within the corporate leadership. When a champion knocks on a senior company members door, they must be taken seriously. Empower them. Have their back when other stakeholders stand in their way. If their tenacity holds, and they manage to get agile analytics through in your company, ultimately you will be seen as the backer of the champion, and not a barrier. You will benefit as much from their success as they do.


This sounds awesome. Let’s get started!


Yes it does! If you are a senior member of a company, no matter the size, get on board. Don’t just get out of the way but get behind your champions.


If you are a more junior employee, this is your shot. Become the champion of agile analytics. Become the champion of change because ultimately, you will be established as the expert in your company. You will gain increased visibility to senior leadership who will acknowledge that the coming success, no matter which department, will be attributed to you.


Applying Agile Analytics to Open Thinking

Steve BurgUncategorized0 Comments


Open thinking can be vastly improved by applying the agile analytics architecture.


Open thinking is one of the most commonly used building blocks when companies begin to build platforms nowadays. Companies like Amazon rely on open thinking to distribute information across their operations, marketing, finance, and technical teams. It is built for rapid expansion and scalability, allowing its proponents to constantly be in a growth state without any need to slow down.


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However, like the prior building blocks, agile analytics will be vastly improved by applying the agile analytics architecture.


What is open thinking?


Open thinking relies on one core tenet: all of your data is stored in one place. Sound familiar? It should. We call this repository a DMP (data management platform), and it is one of the required pieces to a successful agile analytics architecture.


How do open thinking and agile analytics differ?


You might be tempted to say that open thinking and agile analytics sounds very familiar. You’d be right. They are familiar but differ in one vastly important aspect.


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Agile analytics works across all available data: 1st, 2nd, and 3rd party data. Open thinking was developed to optimize the workflow internally within a company, giving all parties access to all of your 1st party data.


How does agile analytics help open thinking?


Agile analytics makes open thinking behave in a much larger way by bringing in further data sets to bolster the initial data. The 2nd and 3rd party data will allow us to broaden the capabilities of the 1st party data and ensure accuracy.


Let’s take the Amazon example and apply it to a product they already have going called anticipatory shipping. Anticipatory shipping uses an algorithm powered by Amazon’s 1st party data to ensure that they always have products in areas where they are going to be needed.


In open thinking, Amazon is connecting to their DMP and using past sales data combined with user and geographic information to make sure their local warehouses always have an appropriate supply of products in demand. So when you need toothpaste, Amazon already has toothpaste nearby and waiting.


Let’s apply agile analytics to the same algorithm and see how it improves. Let’s add in Scarborough 3rd party local demographic data combined with Experian segmenting and BlueKai web usage. We’ll also throw in some 2nd party social data from Facebook, Twitter, Instagram, and Pinterest.


We now have information that will not only help us figure out what items their customer might need a second time, but also what items which will be purchased in the near future. How? We’ll know what items they are loving on social media, articles they’ve read about specific items, what their local demographics are like, and what others in their demographic profile can afford.


Using the above information, we can now not only ensure that the items are in stock in local warehouses, but we can use the same data to inform on platform campaigns. We can show consumers the products we know they are going to be interested in and ensure the consumer can afford them.


The above example is one of many but as you see, bringing in more data can only expand the capabilities of your company, no matter what your process is. Data is something we should embrace and use across every facet of our companies to ensure greater success.


Applying Agile Analytics to Platform Thinking

Steve BurgUncategorized0 Comments

Platform thinking can be vastly improved by applying the agile analytics architecture.


In our last post, we discussed applying agile analytics to the different building blocks of digital transformation. By applying the architecture of agile analytics, we were able to vastly improve the first building block of digital transformation: design thinking.


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In this post, we will make the next building block of digital transformation significantly more efficient: platform thinking.
What is platform thinking?


Platform thinking is the brainchild of Sangeet Paul Choudary as initially expressed in a post he wrote on medium in 2013. Platform thinking states that every business is an engine. It needs to do a certain set of things repeatedly to create value. If you haven’t figured out that set of repeated operations, you probably haven’t created a scalable business yet.”


There are three basic steps to platform thinking:


  1. Creating the engine: Figure out the repeatable steps necessary to create value.
  2. Oiling the engine: Test and optimize the repeatable steps to refine the process.
  3. Stepping on the gas: Repeat these steps over and over again for more and more clients until you’ve reached scale.


Where does agile analytics come into play?


In the same article on Medium, Choudary lays out the three ways a business can run using this formula: Using humans, algorithms, and users.

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An example of each is:


  • HuffPo: Editors decide the content to prioritize each day.
  • Google News: Algorithms decide the top news of the day.
  • Twitter: Users’ tweets and retweets decide the top news of the day.


Agile analytics can optimize each and every step along the way regardless of the use case. In fact, in the agile analytics architecture, we like to fuse all three business cases together.


How do we do this? Let’s create a new content site. One that is powered by humans, optimized by an algorithm, and which takes into account how users are engaging with your product.


This would be a new type of content site, one that many publishers are currently experimenting with. Since machine learning algorithms require massive amounts of data to optimize, the site would take a mix of first and third party data to inform the human editors which content to create.


Applying agile analytics


In this example, our first party data is the site use itself. Our third party data would be general internet usage which you can get from companies like BlueKai and even second party data from Twitter.


So where is the user factor here? This is where it gets fun. Based on how users interact with the site, we could use cookie data to create a custom built site for each and every user. This would factor in data from other similar users using first party segmentation data with act alike audiences. It would also use your own browsing data to ensure the content you are getting is relevant to your interests.


Platform thinking does not disappear with the advent of agile analytics but it continually evolves. Instead of three separate thought processes, we have merged them into one and created a hybrid model based on platform thinking that is buildable, testable, and scalable. It is by all definitions, the Toyota Prius of the product design world. We have taken the best of what we had and merged it together to create a longer lasting, better performing, and more customizable platform using the same processes.


Creating Digital Flow for Agile Analytics

Steve BurgUncategorized0 Comments


In order for agile analytics to work properly, the correct digital flow must exist.


Executives put large sums of money into marketing their technology. In fact, companies spend an average of $7.4 million on data-related initiatives in 2015. However, most of this investment created more digital data silos. We have Google Analytics for web traffic, Omniture for segmentation, Salesforce as a CRM, and countless other programs — all producing data. The issue is that none of them communicate with each other.


Establishing a digital flow that will ensure optimized performance requires a different digital architecture for each organization. It must be dynamic, able to shift as the business does without interruption, as well as provide opportunities for innovation as the data dictates.


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While building this philosophy of agile analytics, we came across an amazing ebook from Forbes that discusses this exact issue. They break it down into three essential building blocks: design thinking, platform thinking, and open thinking.


For this post, we will concentrate on design thinking and follow up on the others in separate posts.


Design thinking


Design thinking, as described by leading proponent Tim Brown, president and CEO of IDEO, is “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”


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In other words, placing design at the beginning rather than the end of the process. The next step is, how do we apply this to the digital flow necessary for agile analytics?


Design thinking has three major stages, each can be applied to the agile analytics digital flow:


  1. Invent a Future: Set goals about what you want to get out of your company’s data that you are not currently getting. Rather than poll employee for this information, simply observe how the data is being used and look for other opportunities for application.
  2. Test Your Ideas: Start slowly bringing new data to different teams. Watch how they respond to the new data sets. Adjust the data, data sources, and other variables accordingly until it becomes useful.
  3. Bring it to Life: When you’ve got a winner, identify the activities, capabilities, and resources your company will need to produce and distribute the new digital flow to the rest of your company.


How does this work in real life?


A great real life example of design thinking using a data focused process comes from P&G’s Oil of Olay. When executives were looking to rebuild the brand they took a look at the two primary locations that were selling the brand: mass retail channels and high-end department stores. They observed their customers in real life and came to a startling realization.


The industry was targeting women over 50 who were primarily worried about wrinkles, while ignoring the audience of 30-50 year olds who had other skincare concerns. The product and the market were not aligned.


As a result of this discovery, they used data to test other skin products that had multiple uses and protection capabilities.


That’s how it was done years ago.


The P&G process meets agile analytics


Using the agile analytics model, we can update this process and rework it completely to make this decision take hours instead of weeks. First, we would pull our 1st party credit card data to see who was purchasing skin care products and where. Second, we would overlay 3rd party data from any number of companies to learn what was important to those making purchases.


We can then test the new products in key markets, taking into account outside parameters (age, income, packaging, store placement, etc.), after which we would roll out the product nationwide.


The whole process is significantly shortened. We don’t have to spend weeks observing people in stores. We have the capability to see exactly who our customers are and decide if they are the customers we want. We know who is going into department stores, who is purchasing online, and we can overlay 3rd party data to figure out the why.


The above is just another example of the efficiency of agile analytics. Design thinking just got a substantial upgrade.


What is Data Visualization?

Steve BurgData0 Comments


What is data visualization? In short, it is a way of making large, complex sets of data easier to understand, in a way that is different than visual analytics.


In our last post we spoke about visual analytics and there was some concern that people in the digiverse would think that Jaco is a data visualization company. Once we got into the topic we realized that defining both terms was not only necessary, but each topic deserved it’s own post. We are big believers in data visualization and we employ the examples all the time here at Jaco.


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What is data visualization?


When you google the term data visualization, you get the following definition, “data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.”
That definition is quite broad, so we came up with our own:
Data visualization is a term that describes the practice of transforming data into patterns and shapes, making it easier for people to understand.


This is very different from visual analytics which is showing you how people use your products.


Examples of data visualization


Data visualization comes in all forms of shapes and sizes, ranging from complex patterns and shapes to ones we commonly encounter. Those who work in analytics constantly encounter visualizations in the form of line graphs, bar charts, pie charts and more. We could go on, but Marshall Erickson explains it best.

Taking data visualization to the next level


While we usually encounter very simple visualizations, there is a whole field of science dedicated to building extremely beautiful and complex patterns based on data. In fact, two data professionals had a year long correspondence entirely through data visualizations.


The result was so impressive, it has since been acquired by MoMA and made into a book called Dear Data. It’s a fascinating story, and a recommended read for anyone interested in data visualizations.


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Another great example of complex data visualizations, Flickr user 802.11 used Wikipedia to created a stunning visual timeline of the band Pink Floyd from 1960-2000.


Pink Floyd Timeline 1960-2000


While the splotchy pattern and bright colors resembles purely aesthetic abstract art, the designer actually used data to build the entire piece. If you look closely, you’ll see a color map on the bottom of the timeline that gives each member of the band a color. The shapes and colors directly correlate to the years and albums and band members involved.


Data visualization makes large data sets easier to understand, however, it is quite different from visual analytics. While data visualization relies on data that already exists in the world. Visual analytics is a data collection mechanism that allows you to collect information on how users are engaging with your platform.


What is Visual Analytics?

Steve BurgData0 Comments


When WalkMe purchased Jaco, we were referred to as a visual analytics platform. For those who work in the data field, this may have been a bit confusing as there is already a concept of data visualisation. While we will dive deeper into that topic on another day, this post will be focused on what visual analytics are and how they are used.


What is Visual Analytics?


Simply put, visual analytics platforms give you the ability to see not just who is using your platform (quantitative data), but also how they use it (qualitative data). While the quantitative data can tell you the who, what, and when, visual analytics providing you with valuable answers to the question of why. Using our software, you can view a pixel perfect recreation of every single user session that occurs on your platform.


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How is Visual Analytics Used?


Visual analytics can be applied in multiple ways and as we discussed in earlier posts are an integral part of the agile analytics formula. The two primary cases we’ve seen for visual analytics are used by the product and support teams.


Visual Analytics for Product Teams


While building the Jaco product, we engaged with product teams from companies across various verticals to ensure that we were answering their needs. The remark we encountered most often was, “I want to know exactly, and I mean exactly, how my users are engaging with my product.” That is what we set out to tackle.


After lengthier conversations, the real need became clear. Product managers spend, time energy and money building features for a product. As a result, it is crucial to identify which of the features they are being used, how often, and for what. They were eager to ascertain the success of existing features in order to determine the success of future products.


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From the agile analytic side, this is some serious 1st party data. Imagine having every department pointed towards a similar goal and how much more efficient that would make your company. It’s an unfortunate question to ask, but how often do you believe the marketing team receives use case examples (outside of case studies) to build out their targeting plans? From our experience, not close to often enough.


Visual Analytics for Support Teams


Not to toot our own horns here, but Jaco is one of our favorite implementations of agile analytics. In order for visual analytics to be used to it’s fullest potential, the data must be able to be used for multiple applications that are related but different.


So why would a support team need to see a pixel-perfect rendering of a user session? Simple. If our tech support teams were able to see exactly what a user did when something went wrong, it would eliminate the guesswork from tech support. This would drastically reduce support ticket times.


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The perfect example of agile analytics in action is a customer of ours, Unpakt. You can read more about their case study here but let’s focus on the part that matters. “While our support team does not rely directly on Jaco, it is utilized in our support and debugging process. By avoiding human error and imperfect memories, the ability to truly understand what triggered the event likely saves 90% of the effort to create a solution.”


When Should You Use Visual Analytics?


Now that we’ve examined what, how, and why, let’s discuss when visual analytics should be used. To us, it’s really quite simple. If you want to learn more about how your product is really used, you are ready for visual analytics.


There are many types of visual analytics, but at Jaco we believe that if you truly want to understand your customer, you need to step into their shoes. Jaco’s is the only technology that allows you to recreate your user’s experience. Heat maps may show you where they click, but it doesn’t tell you why, which is why we don’t offer them.


We believe that understanding the why adds a whole new layer of data to your aggregated field and answers many of the questions that our clients will ask.


In summation, visual analytics is quite different from data visualisation. It adds another dimension to the product analytics, one that will make entire companies more efficient.


Re-Imagining a Classic Jingle, Now Powered by Data

Steve BurgData0 Comments

jaco data driven classic jingle


In 1974 General Motors put out a commercial that hit the golden standard for advertising; it became a cultural icon. Ask someone anywhere in the world what America’s favorite pie is. There’d be good money to win on a bet that the answer is apple.


Chevrolet’s commercial began by asking: “America what’s your favorite sport?” The answer was baseball. “Sandwich?” Hot dog. “Pie?” Apple. “Car?” Chevrolet.



This commercial ran from 1974 to 1976. Chances are that if this data was verified, it was done through survey data and not using 1st party sales data. So, let’s use modern data methods to rewrite America’s favorite jingle.


Is baseball still America’s favorite sport?



America’s favorite sport at one point was baseball. However, as a decline in attendance and television ratings began to emerge, the question was asked: Is baseball still America’s sport?


Let’s take a look at some 3rd party data, which is considered to be better for this particular occasion. Why? It’s impartial and cannot be messed with by a 1st party TV station group or sports league.


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For this we’ll use Nielsen ratings, collected through a hybrid model of digital set-top box data and survey diaries. It’s not perfect since it’s not all digital but it is considered to be the current market standard. So, our rankings are as follows:


  • Super Bowl (American Tackle Football): 45.3 US Household Rating Points
  • NBA Championship (Basketball): 11.3 US Household Rating Points
  • World Series (Baseball): 21.8 US Household Rating Points
  • Stanley Cup (Hockey): 3.2 US Household Rating Points


So, while the most the World Series was the most watched baseball event of the the past 25 years, it does not dethrone football. That’s one change for the jingle.


Are hot dogs still America’s favorite sandwich?



Sandwiches: One of the most popular ways to eat food in America. Your plate is also food. Wonderful.


Hot Dogs were the most popular sandwich filling in 1974, but all things change. According to the latest 3rd party data from Datassential (2014), the most popular sandwich type was not the hot dog or even a sausage. That came in at #18. The current most popular sandwich in America is… Turkey!


Our jingle is shaping up quite nicely now, so on to pies!


Is the apple pie still America’s favorite pie?



Pie. A classic american dish. One of the most popular holiday dessert items. So much so that America’s #1 retailer, WalMart decided to see what the most popular pie flavor was during the 2015 holiday season. Jumping into their 1st party sales data WalMart pulled out a new pie champion, Sweet Potato Pie. Or, more specifically, Patti LaBelle’s Signature Sweet Potato Pie.


Is Chevrolet still America’s favorite car brand?



Finally, America’s favorite car brand. Chances are that this was not even Chevrolet at the time, but they claimed it in the jingle so we will have to update that as well!


Auto brands are a lot more than just cars nowadays. This has been taken account by the folks over at Business Insider. When they released their best selling vehicle report it covered cars, trucks, SUVs, Cross-overs, and others. Basically, all of the vehicle types. The best selling car of 2016 was the Ford F-Series followed by the Chevrolet Silverado.


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The Data Driven Classic Jingle


So if we had to re-create this classic jingle today, based on 1ict and 3rd party data, it would read something more like the below. Feel free to sing along with us!


“America, what’s your favorite sport?” Football!


“Sandwich?” Turkey!


“Pie?” Sweet Potato!


“Car?” Ford!


We love football, turkey, sweet potato pies, and Ford. They go together in the good ol’ USA. Football, turkey sweet potato pies, and Ford.


Nah, just doesn’t have the same pop.


Data Analytics Systems Work for B2B Companies Too

Steve BurgData0 Comments

We’ve now seen how data is evolving to transform organizations in the retail world, but what about a B2B company? Let’s discuss how a B2B company can benefit from streamlining all their data analytics systems into one system.


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While the structure of the information flow here is very similar to retail companies, albeit with different goals, the data generated is applied differently on a tactical level.


Let’s begin with the data structure:


B2B Data Analytics Jaco WalkMe


First, collect data from multiple sources


We have two initial points of data collection. We collect data when a user visits the site and when they fill in the lead form.


We can use the data factors from when they visited the site or landing page to help qualify the lead as we learn more about how other qualified leads have visited the site or landing page.


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How long did they spend on the site? How many pages did they visit? What elements did they click on? All of this is instrumental in finding common behaviors amongst leads.


Then, streamline information into a data analytics system


After a lead comes in, we can use 3rd party data to find behaviors that are most likely to occur within a lead. For example, if we use Experian clusters along with data analytics systems, this is data that can be passed through to the pre-sales and sales team. They’ll know a lot more about customers and it will help make personas statistically a lot more relevant.


Depending on the data analytics systems you use, there is also a chance you can see a bit about the sites your potential customer visited prior to your site. This could give great insights into the other products your customers are considering.


Next, use data analytics systems internally


On the product side, we should be able to track exactly how our customers use the product and where they came from. We can use a combination of lead form data and usage data to build out product extension lines. Perhaps customers like these purchased or upgraded to other products your company may sell as well.


Any data we find internally should also go back into the DMP so that the marketing team can try and target external users. Depending on what is important to you, these insights can be pulled into your marketing campaigns to try and draw in leads that behave more like your most active users or maybe your largest contracts, again depending on priority.


Finally, the marketing team optimizes on this information


Thanks to a lot of the technology available on the market, almost any data can be used to build increasingly efficient marketing campaigns.


Find your most valuable users and combine that 1st party data along with 3rd party data and you will find that in the right algorithm, your cost per lead will drop dramatically while the lead quality becomes significantly better.


Central Data Analytics Systems Improve the Bottom Line


The end goal and result of a central data analytics system is ultimately to make our companies not only more informed about the people who buy our products but to make us increasingly more efficient.


Data is going to help us reduce support ticket times, decrease user confusion, decrease lead cost, increase contract size, and make our companies more valuable.


Isn’t that where we’re all trying to get to anyways?