Something we should have started doing a long back is sharing the success stories of our clients. We’ve done so many cool things together and for our clients that these stories are definitely worth sharing, and reading.

The main motivator behind starting this series has been the fact that too many companies are struggling to see value in things like better data, more data, more advanced analysis, marketing data warehouses or broken data silos. And we got no one else to blame than ourselves. These are all rather complex topics and should be explained by vivid examples, success stories and case studies.

As we, at Reflective Data, are in most part working for enterprises and SMEs, giving them competitive advantages, often working with sensitive data and our work is almost always covered by NDAs — most of the case studies in this series will be anonymous. That being said, though, we are doing our best to give our readers as much context and extra information as possible to make the case studies relevant, relatable and applicable to others.

To give you a few ideas on what kind of case studies we’re going to publish, here are a few sample topics.

• Sending data from all marketing tools, CRM, and customer support tool into a data warehouse for a large e-commerce business
• Building a custom AI-based product recommendation engine using data from Google Analytics stored in BigQuery
• Solving the problem of data discrepancy between the AB testing platform, Google Analytics and backend/finance numbers
• Building an interactive roll-up reporting dashboard for the management of a lead-gen business with multiple websites/markets

Make sure to subscribe to our newsletter to receive information about the latest case studies and other relevant content published on our blog.

All case studies will be available on this page.

The post A Series of Digital Analytics Related Case Studies Coming Soon appeared first on Reflective Data.

First of all, thank you Google Analytics for sharing our content with your audience!

Secondly, thanks to Michael Howe-Ely for the idea. Check out his list.

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Measure Long-Term Metrics Like Customer Lifetime Value (LTV) Using Google Analytics

In this article, we are going to explore some of the ways we can leverage Google Analytics to track churn, LTV and other really useful metrics.

"Metrics like churn are vital for SaaS and subscription products but any company that gets return business should have their long-term KPIs in place"

Measure Long-Term Metrics Like Customer Lifetime Value Using Google Analytics https://t.co/HozsK37gyJ by @reflectivedata #measure pic.twitter.com/OMLcHujHsF

— Google Analytics (@googleanalytics) May 26, 2021

Why Every Business Needs a Marketing Data Warehouse and How to Set One Up?

The number of marketing tools an average business uses has grown rapidly. Besides one or two analytics platforms there’re a few ads platforms, CRM, CMS, several social media platforms, an email system and probably a few more tools and platforms. Besides a lot of valuable data, this also means a lot of silos. A marketing data warehouse can help you break those silos and maximize efficiency.

“Data silos create confusion and disagreement between teams, leading to a situation where, at the end of the day, no-one knows which tool or numbers to trust.”

Why Every Business Needs a Marketing Data Warehouse and How to Set One Up https://t.co/Y7vie4LHcY /by @reflectivedata

— Google Analytics (@googleanalytics) May 22, 2020

How to Query and Analyze Google Analytics Data with BigQuery

BigQuery is an extremely powerful tool for analyzing massive sets of data. It’s serverless, highly scalable and integrates seamlessly with most popular BI and data visualization tools like Data Studio, Tableau and Looker.

How to Query and Analyze Google Analytics Data with BigQuery https://t.co/GGsbOPdiwt /by @SilverRingvee for @reflectivedata #measure pic.twitter.com/jWa1TbW9kS

— Google Analytics (@googleanalytics) April 16, 2020

Data You Should Be Tracking Using Google Analytics Custom Events

Out of the box, Google Analytics already tracks a bunch of really useful data points. What the default setup lacks, though, is context and events that are specific to your website and business.

Custom Events provide a perfect solution for adding context and tracking more specific user actions

“Custom Events provide a perfect solution for adding context and tracking more specific user actions.” This article from @reflectivedata provides ideas for custom events you can implement on your own and/or your clients’ websites. https://t.co/qP8ouq5iaA #measure pic.twitter.com/yICU3j08Pe

— Google Analytics (@googleanalytics) November 6, 2019

Tracking Rage Clicks Using Google Tag Manager and Google Analytics

Visitors rage clicking on certain elements on your website is a good indicator of a UX error. For example, people may click on a blue text that is not a link or on an image that has no click functionality.

How to Detect Unhappy Users And Their Rage Clicks Using Google Tag Manager and Google Analytics https://t.co/QhKseIg4xA /by @reflectivedata #measure pic.twitter.com/XUReBqHTtF

— Google Analytics (@googleanalytics) September 17, 2019

Creating and Using Custom Alerts in Google Analytics

In this post, I give a complete overview of why and how one should use Custom Alerts in Google Analytics. There is also a list of useful alerts you should consider adding.

Creating and Using Custom Alerts in Google Analytics https://t.co/U5g1qutgTf /by @reflectivedata #measure pic.twitter.com/2XGH2vU94h

— Google Analytics (@googleanalytics) September 4, 2019

Tracking Common User Actions Using Google Analytics Goals

Too many companies add the Google Analytics snippet on their site and that’s their whole tracking setup. Tracking some common user actions using goals is the first step one should take after installing the snippet.

Tracking Common User Actions Using Google Analytics Goals https://t.co/NtAm3RisUF /via @reflectivedata #measure pic.twitter.com/76oXKaO7bc

— Google Analytics (@googleanalytics) August 8, 2018

What is gtag.js and when should I migrate?

Now that gtag.js is a standard setup, most of the new websites will be using it. But if your setup is a bit older, should you consider moving to gtag.js?

What is gtag.js and when should I migrate? https://t.co/xFQV3bvyjt /by @reflectivedata #measure pic.twitter.com/hujKn60a3d

— Google Analytics (@googleanalytics) January 24, 2018

Deciding Which Google Analytics Goals You Should Create

In Google Analytics you are limited to 20 goals. In this article I help you decide how to spend those 20 slots in the most efficient way.

How to Decide Which Google Analytics Goals You Should Create https://t.co/20KSIB1KHj /via @reflectivedata #measure pic.twitter.com/vzh6qxJPoX

— Google Analytics (@googleanalytics) November 14, 2017

Google Analytics Audiences, Acquisition and Behavior Reports Visualized in Data Studio

Google Data Studio is a really powerful and flexible BI tool that lets you easily explore and visualize your data. If you are just getting started, this post is for you!

Google Analytics Audiences, Acquisition and Behavior Reports Visualized in Data Studio https://t.co/LEwEacQU2g /by @reflectivedata #measure pic.twitter.com/szUGEYTQkp

— Google Analytics (@googleanalytics) December 2, 2017

How to Decide Which Google Analytics Goals You Should Create

In Google Analytics you are limited to 20 goals. In this article I help you decide how to spend those 20 slots in the most efficient way.

How to Decide Which Google Analytics Goals You Should Create https://t.co/20KSIB1KHj /via @reflectivedata #measure pic.twitter.com/vzh6qxJPoX

— Google Analytics (@googleanalytics) November 14, 2017

We are really thankful to Google Analytics that they’ve shared our content with their followers and we’ll continue creating relevant and helpful content.

Photo by Marten Bjork on Unsplash

The post List of Our Blog Posts Shared by Google Analytics appeared first on Reflective Data.

•     Data analysis is giving small businesses the opportunity to be even more competitive through the use of analytics.
•     Artificial intelligence and machine learning are disruptive technologies that are revolutionizing the landscape.

Big data has become adopted by more companies in recent years. We’ve seen the demand increase from 17% to 59% in just three years! As a result, businesses that use data analytics experienced an increase in profit that reached as high as 10%. Furthermore, those same businesses experienced a reduction in costs that also reached as high as 10%.

Data Analysis is helping companies make smarter decisions that lead to higher productivity and more efficient operations. It provides a significant competitive advantage.

Data Analysis is Making Smarter Decisions

Small businesses are experiencing the greatest impact of analysis, and this is not expected to slow down. The truth is that if your business does not follow through with these trends, then you’re going to find yourself at a significant disadvantage. It has become the cornerstone for all strategic business decisions.

Finding the right audience is an important step to making better decisions moving forward. Business analytics gathers data from popular hubs like Facebook and Instagram, and this data is used to create a demographic of a brand’s ideal customers. In turn, this profile determines what types of features your customers want or need from specific products. So it is a powerful tool while deciding about how to improve current products and services; this is a powerful tool.

Using Data Analysis to Make the Most out of Consumer Patterns

Today’s businesses must know what their customers want to make the proper decisions moving forward. If the brick and mortar stores are not stocking the right products on their shelves, then they are going to experience a decrease in sales. If online providers are not offering the right services, then they will lose customers. The first step in business is to make sure you’re selling the right products to the right people.

That’s where business analytics comes into play. It provides the information necessary to make sure that your business is providing the right products and services. This process is known as predictive analysis and uses four methods:

•     Segmentation: Uses information about target customers to split them into separate categories based on demographics, behavior, and attitudes. Then specific products or services are targeted to these segments.
•     Forecasting: Uses analytics to predict specific patterns that allow businesses to understand the demand for a product or service beforehand.
•     Pricing: This is the process of analyzing data from various sources – usually competition – to determine how much a target market is willing to pay for a specific product or service.
•     Customer Satisfaction: Improving the customer journey is important, and customers today are not afraid to share what you’re doing wrong. Use this data to improve their experience.

In this era, consumers hold all of the power in business. A company must conform to the needs of its customers, or else they will be ignored. Customers expect preferential treatment. More importantly, consumers provide so much information that they expect businesses to know their patterns. Analytics allows for better planning and insight based on the patterns of their customers.

Companies that fully utilize their customer behaviors to make decisions outperform their competition by a whopping 85%! Those same businesses also experienced an increase of 25% in their profits. It shows us that analytics is powerful because it identifies buying patterns. This information is then used to make important marketing decisions.

Data can Drive Performance

Small businesses can expect to spend a considerable amount of time analyzing data to identify buying patterns, but it is just as important to focus on performance. Data analysis plays a vital role internally within a company by providing insight into decision-based on improvements in efficiency. The idea is to streamline these business operations so that they are more time-efficient. Some examples include operational costs, product development, and workforce planning. Using insight provides a unique insight into complex internal business scenarios.

Businesses can use analytics to improve their profit margins by developing more efficient processes.

Risk Mitigation is Improved through Analytics

One of the biggest reasons why businesses need to use analytics to make better decisions is due to the risk being posed by the sheer amount of data being gathered. There is so much unstructured data being delivered that it’s easy to make the wrong decisions unless it’s properly analyzed. With that said, having the right data analytics strategy in place will predict risk and help make better decisions moving forward.

Business analytics also makes expansions much less risky since businesses have access to valuable information before they make their final decision. It’s also possible to interact with the information so that it can be used to create an actionable plan.

Companies that have a baseline standard for measuring risk are going to be able to incorporate exact numbers into their decision modelling process. In short, they can predict certain scenarios and plan for them in advance.

Final Thoughts

Data insights are a disruptive technology, so businesses must be prepared to keep their systems up to date. Small businesses must be able to identify new opportunities as quickly as possible because it provides a significant competitive advantage.

Businesses must stay focused on analytics because data is a valuable aspect of making core business decision in today’s market. It allows businesses to stay in front of this digital disruption, ensuring continued success. Companies like the Research Optimus helps businesses to better decision making through data analytics process.

The post How Data Analysis Improve Decision Making appeared first on Reflective Data.

This is why plenty of people remain uncomfortable about the prospect of applying it to anything other than financial or campaign-related matters. Conventional wisdom resists the treatment of workers as utilities, and doesn’t like reducing productivity to simple facts and figures.

But this is based on a myopic and outdated view of what the analytics process involves. It doesn’t fully supplant human opinion, nor does it render shallow judgments about workers. Instead, it’s merely a tool: something to supplement human efforts.

Used well, analytics can significantly improve team productivity and performance. How does this work? Let’s take a closer look at this core element of modern business:

They can help efforts to raise morale

Team productivity is about far more than the qualities and skills of the individuals involved. It’s also about how the team members feel, and how they interact. Morale — both individual and on a team basis — is a mission-critical concern, because an unhappy team will struggle to perform and become stressed very easily.

So, how can analytics help you boost morale? There are several ways:

• They can showcase successes. It’s always important for a business to celebrate when things go well, but there isn’t always some obvious profit figure to bring up. With analytics, you can get a more granular look at how things are going, and pick out the precursors to that type of success eventually arriving (the small wins). For instance, you could celebrate a significant uplift in search rankings for important terms, knowing the corresponding uplift in conversion that’s likely to follow.
• They can highlight employee strengths. People like being praised, and while you should certainly look for weaknesses to improve upon, you should also find the strengths to highlight. It might be something as simple as one employee yielding more value per hour, hitting a certain number of tasks completed in a day, or getting exceptional comments from customers on a consistent basis.
• They can guide feedback surveys. Getting full feedback from employees is vitally important, because no amount of analytics can tell you how people feel — but your data can help you create better surveys. For instance, if you notice that a particular process is quite slow, you can ask specifically-related questions, then use the answers to make meaningful improvements that wouldn’t have been possible had you not known about that issue before consulting people.

They can identify operational roadblocks

Digging into the metrics of your everyday operations is a great way to glean insight into the holdups: the snags along the way that prevent everything from running optimally smoothly. For instance, you might notice that one of your internal processes is taking markedly longer than comparable processes — closer inspection might reveal an easy-to-fix bug.

Analytics can also help you monitor where employee time is going, information that can be very informative when it comes to attitude. For instance, you might discover that one of your employees whose role is to write copy actually spends a lot of time handling HR disputes, or that your head of finances is consistently distracted by requests from your design team.

In response, you can try some classic A/B testing when it comes to team composition and organization (provided you know how to interpret the results). Make some alterations, let people work for a few weeks, then compare the analytics of that period to those of the period preceding it. Keep doing this, and you’ll figure out the team arrangement that gets the most work done.

You can also use this data to identify areas that warrant investment in automation. Losing a lot of time handling admin for your scheduling? Try something like Calendly or x.ai to make your meeting easier. Picking through payroll on a manual basis? Given modest SaaS pricing, something like Wave’s payroll software will more than return its value given the chance.

They can support clearer incentives

Properly incentivizing performance is essential for keeping performance at a high level year-round — without it, your employees will start taking opportunities to lessen their workloads, lowering their productivity and paying less attention to what they’re doing. It won’t even be intentional in every case. Dedication is a hard thing to maintain, and people can burn out.

Because a well-implemented analytics system gives you a deep and broad perspective on everything that’s happening with your business, you can discover which elements are going well and which are underperforming. You can then set simpler metric-based incentives using the S.M.A.R.T system, e.g. improve metric X by 10% in two months and receive reward Y.

It’s far better than asking for general improvements and gauging them subjectively, as employees aren’t likely to respond to that approach. It’s not pleasant to put a lot of work into something but have a promotion denied because your boss doesn’t believe in you. Far better to let the data speak on your behalf.

Implementing a system for collecting and parsing comprehensive analytics won’t solve all your problems, nor will it push anyone into redundancy — but it will give you the added insight you need to make your team members happier, less stressed, and more motivated. Why wouldn’t you want that?

The post How Analytics Can Help Improve Team Productivity & Performance appeared first on Reflective Data.

For that, you first need to understand big data a bit more. As the term itself suggests, big data is nothing but a vast volume of information that businesses receive daily in structured or unstructured formats. How and in what quantity this data arrives is not as important as how these are used. The use of the term “big data” is relatively a recent phenomenon, but if you are aware, business houses have long been collecting and storing data in large quantities since eons; just the number of such sources which supply data has increased encompassing social media, in-app purchases, online shopping behavior, etc.

Big data helps SEO

SEO practices depend on the use of large quantities of data, which is nothing but big data. As you know, search engine optimization is an endeavor to enhance the search result rankings of a website based on the availability of online data. The search engine giants like Google direct visitors to pages that appear to be relevant or authoritative. Whether a site is trustworthy or not is determined by the number and quality of links it gets from other websites. That’s why an SEO expert makes every attempt to develop content that is authoritative to gain a higher position in the rankings.

The role of SEO in marketing campaigns is well-known to everyone, but its techniques continuously change due to the evolution of big data. Here is a brief insight into how big data is contributing to improving SEO practices.

Content

Online content refers to any amount of published information, and every page of content is quantifiable online. Search engines analyze these content pieces and direct visitors to the relevant results based on their searches. Now, the catch is what a search engine may think to be most important for a query may not be favorable for your business. This situation can be taken care with online reputation management services. For that, a digital marketing firm needs to work on the visibility of their client’s business and bring positive content on the top by pushing negative details down.

Social Media

Social networking sites churn a large amount of data, which search engine giants cannot afford to ignore. And this has been possible because so many users in large numbers continue to join these social channels over time. You get proof of this when you look at the user base of social media sites like Twitter, Facebook, and so on. That’s why enterprises are focused on improving their presence on these social platforms to improve their rankings in the search engines.

Intelligent machines

Why big data is so useful is because it emphasizes more on people than search engines. It lets you take a peep into the buying and browsing behavior of your customers. And since things are transparent online, people also don’t hesitate to accept cookies or elicit their check-in details of specific locations on social media.

Since the user-driven data carries weight, computers and mobile devices are also becoming intelligent and faster in their responses to search-related queries.

Trust factor

As time passes, big data will soon become a regular part of everyone’s life where no one would have hesitation to share details. Online advertisements customized to suit different needs will be acceptable and will help make lives easy and comfortable. The dependence on digital devices will grow, which necessarily would lead to the generation of more accurate big data for various needs. However, this doesn’t encourage spamming. Only the sources of marketing information will change.

Customer experience

Big data will lead to better customer experiences and business results. Search engines will be empowered to fetch targeted results based on preferences, choices, and location details. It is already happening in the e-commerce businesses, though. But things will become more specific in the coming days.

Deeper SEO insights

Search engines convert website content into quantifiable data. And in coming days, these will be able to produce more accurate results, which marketers can use for insights. It is because of big data that SEO applies different techniques, such as keywords, on-page optimization, linking, to reach out to their customers. All the efforts combining local SEO, content marketing, and mobile data will help generate accurate user insights, and this can be possible only because of the contribution of big data.

Today, big data has become a powerful SEO tool for businesses. Companies can sift through a large volume of data quickly to understand what performs better for them. They can primarily obtain a 360degree view of the customer, right from what they are looking for to what they prefer more. User behavior becomes more comfortable to locate. From this, it is not difficult to understand why companies would depend on big data for their digital marketing practices. After all, big data help them in better targeting of their services, improving customer experiences, and increasing their visibility in the relevant marketplace. When all this happens smoothly, operations and revenue will naturally grow.

So, if you have been wondering why big data is critical for SEO practices, you need to reconsider your observations once. When big data marries SEO strategies, it can conquer all the hurdles and give your company the desired visibility and wider reach to its users. The chances of conversion will also improve.

The post How Is Big Data Impacting Search Engine Optimization appeared first on Reflective Data.

How do you know which subject lines are most effective? Do you know if a traffic boost is from the last email you sent or a previous one, or is it from something else entirely? Are people even reading the unique content you send out?

That’s where marketing key performance indicators (KPIs) come into the equation. They are metrics that highlight improvements, successes or even failures.

KPIs can be used to show what works in an email campaign, what doesn’t and how things can be improved. Metrics can also be used to track performance in all areas of business, including live events, customer feedback or general sales.

If you want to optimize your email marketing campaigns, the following 10 KPIs are what you should be paying attention to.

1. Open Rate

It doesn’t matter what you’re trying to do, be it chasing more sales, generating leads or simply increasing traffic to a site. If no one opens your emails, then it’s all for nought.

The open rate KPI tells you how many people opened your email, read what was contained within and where it ended up. It’s not always reliable, because some email services automatically mark a message as read when you scroll by.

Even so, it’s a valid indicator of who’s interested in your content and who isn’t. Higher numbers reveal the success of a campaign.

2. Click-Through Rate

The click-through rate tells you how many people followed the links contained within your email.

If a tool doesn’t provide this, you can divide the total clicks by the emails delivered and multiply it by 100. The result is the percentage of users following your links.

3. Conversion Rate

While most metrics track specific actions, this one is more about the end result. It focuses on why you sent the email or campaign in the first place, and whether or not your goal was met.

For example, did you want customers to make a purchase, register on your site or browse web content? Whatever it is you wanted your audience to do, how many actually followed through? That is your conversion rate.

It tells you what your campaign achieves and whether or not your audience is interested in its content.

4. Total Sales

The total sales metric involves the full impact of sales as a result of the email campaign. It’s not only about sales directly from email, but also what customers and visitors do as a result. Maybe they didn’t like a product featured in the email, but bought something else instead.

5. Total Number of Unsubscribes

If people don’t enjoy your content, they’re going to unsubscribe. This metric is as important as your new subscriber rates. If you’re bleeding followers, then you’re clearly doing something wrong — and you’ll never know what’s happening if you’re not tracking it.

6. Bounce Rate

Bounce rate is a lot like unsubscriber counts with one exception: most of these events are accidental. When an email bounces, it means either the address in question is not correct, not in use, falsified or a server is down. On some rare occasions, a bounce may happen because an inbox is full.

Either way, this occurs when there is a delivery problem — which means one less person is receiving your emails and improvements are needed.

7. Cost Per Lead

What is the total cost to acquire a new customer or generate a lead? How much are you spending to develop an email, send it out and reply to various communications? Do you have a dedicated team in place, or are you doing it yourself? These are all things to consider.

8. Time on-Site

You may be generating a lot of traffic as a result of an email marketing campaign, but is it truly worthwhile? Are your visitors staying on-site long enough to make a difference? Is it translating to sales or conversions?

9. Contact List Growth

Most marketers have a system in place to collect new email addresses, which are then added to their newsletter or updates list. Over time, it means the contact list for a campaign grows instead of shrinking.

This is an important metric to track. Not only does it tell you how big an audience your emails are reaching, but it also signals just how effective a campaign is.

10. Social Growth

Alongside traffic, you might also be looking to grow social exposure or follower counts through an email campaign. This is another metric to track if so.

KPIs Can Help Achieve Your Goals

Depending on what you wish to achieve, you don’t necessarily have to monitor every KPI on this list — there are others, too. Choose metrics that align with your goals. If you want to boost follower or audience count, then you should be looking at bounce, unsubscribe and open rates. If you want to increase site traffic, then look at click-through rates, time on-site and social growth.

The 10 email marketing KPIs you see here are some of the most important, meaning you really can’t afford to ignore them.

Cover Photo by Hoster on Unsplash

The post 10 Important Email Marketing KPIs You Can’t Afford to Overlook appeared first on Reflective Data.

In ideal circumstances — provided you have the resources, the time, and a plan for separating the wheat from the chaff — you’ll bring in data from all possible sources (your internal analytics, market research, and customer surveys, to name just a few). But how should you structure the analysis efforts? Not every area or metric is worthy of granular investigation, after all.

Well, there’s a whole host of elements that warrant attention, but here we’re going to look at purchase data (everything you glean from the orders your customers have placed). Analysis of this data is absolutely essential to your operation — here’s why:

It will help you adapt to consumer habit changes

The consumer market moves forward, sometimes slowly, but never falling entirely static. New products are released, existing products pick up steam due to promotion or word-of-mouth recommendation, and previous hit items fall out of favour or are discontinued. The more rapidly you can adapt to the changes (and even predict what’s just over the horizon), the more strongly you can take advantage of them.

These changes, of course, don’t happen out of nowhere. A product won’t generally go from popular to written-off overnight — instead, it will gradually lose its popularity before eventually reaching a tipping point at which it might as well no longer be sold. By analysing the purchase data of your customers, you can discern which products are rising or falling in the ranks, and draw inferences about what product types are gaining or losing value.

You might see that sales of skateboarding accessories have risen steadily throughout the last year, for instance, in which case you’d have good reason to think that it might be a niche worth investigating — and if you check Google Trends and discover that interest is rising across the board (for whatever reason), you could make a concerted effort to target skateboarders.

Patterns support enhanced marketing tactics

Marketing is a core concern for retailers, particularly in the online realm where you can’t rely on people merely happening upon your store and wanting to shop there. The more efficient you can make your marketing, the better its ROI will become — you’ll sell more, and spend less to win those sales. And since digital marketing is inherently suited to complex targeting (relying on smart segmentation of customer sets), customer trends are hugely valuable.

For example, if your purchase data shows that shoppers from a particular region or country have spent 35% more on food products, you can dig deeper into that stat to figure out some tweaks to your marketing. Could you market other food products to those people? Is there a specific reason why that area is so concerned with food? There could be a new hit cookery show, or a big trend towards healthy eating.

If you identified the latter as the culprit, you could take advantage by marketing other health products to people in that area (you could even use a product discovery tool like Product Hunt to find more). The more you know about customer habits, the more sophisticated you can get with persuading them to spend even more with you.

The data is relatively straightforward to collect

Some types of data (e.g. customer feedback) are somewhat laborious to collect, because you need to incentivise them somehow and then actively chase them — and even then, you might have a dataset that’s both limited (most people prefer not to comment) and biased (the people most likely to provide feedback are those either very happy or very angry with your service).

Purchase data, though, requires next to no effort to collect. No matter what platform you use, you’ll end up with basic purchase records (they’re necessary for customer accounts and support requests). What’s more, if you use something with rich native data (the Shopify ecomm service has GA’s Enhanced Ecommerce features running out of the box, for instance), then you’ll have tagged funnel stages at your disposal — possibly without even realising it.

Will any ecommerce solution give you a flawlessly-parsed analytics dashboard from the outset? No, of course not — that’s why you’re best served finding someone to help you get everything usefully tagged and categorised. But all the data is there and ready to use, not costing you anything or causing you any strife. Why wouldn’t you make the most of it?

Loyal customers deserve additional effort

Customer loyalty is of paramount importance to sellers of all kinds, but particularly ecommerce sellers. It’s tough to stand out through pricing or product selection in the digital marketplace, and winning someone’s loyalty will give you the edge regardless, so you need to prioritise it. But how do you know which customers to focus on? Simple — your purchase data will tell you.

By checking your customer histories, you can pick out those who have placed the most orders and/or spent the most money with you, then make an effort to serve the needs of those customers. You can reach out to them for direct feedback, ask them what improvements you could make, and even offer them incentives to spend more and/or bring you referral customers.

When you don’t pay much attention to what your customers buy or how they shop, you can’t go the extra mile to cater to those with the most value. Keep loyal customers happy, and you’ll reduce your churn rate, convert on a more frequent basis, and achieve remarkable stability.

For all of these reasons, putting in the time to closely analyse the purchasing habits of your customers is a highly-effective way to improve your marketing, adapt to changing circumstances, and cultivate immense customer loyalty. If you’re not doing this type of analysis already, what are you waiting for?

The post Why It’s Essential You Analyze The Purchase Data Of Your Customers appeared first on Reflective Data.

For example, duplicate information could mean that members of a marketing team call the same person multiple times. Then, a potential lead gets frustrated, and marketing professionals waste time.

Or, duplicate data could give incorrect statistics caused by inflated numbers. If one legitimate user appears in a database several times and that’s a common occurrence, marketers could reach falsely positive conclusions about the effectiveness of a marketing campaign or the reach of a new product.

Here are five things that marketers and other people who work with data can do to stop duplicate data from becoming a pervasive problem.

1. Have Stricter Data Entry Practices

Human error is one of the primary causes of duplicate data. If people aren’t careful enough to input data without making errors, the steps marketers take to check for identical records might fail. For example, maybe there’s a valid email address in the database that’s john.smith123@mydomain.com. But, if a person also mistakenly types that email address in another instance with “.con” on the end, it would not appear as a duplicate.

Marketers should implement quality control measures for data entry. They may include having at least two people check information before submitting it to a database. Problems can also occur if too many people take responsibility for entering data. When they don’t follow all the same procedures, duplication could happen.

Data quality shortcomings arise due to several reasons, some of which relate to other tech tools. If a company uses an optical character recognition (OCR) program to speed up data importation, it’s especially important to have human oversight that boosts quality control.

2. Use Automation to Help Spot Duplicate Data

Automated tools can cut down on the manual labor used when looking for data duplicates. And, options are available for popular applications that store data, like Google Sheets. While utilizing that spreadsheet program, people can depend on a manual formula or install an add-on that looks for multiple instances of the same information.

Becoming familiar with either the manual command or how to use the add-on can help people find duplicated data faster, putting them a step closer to removing it.

Also, when choosing an automated tool designed to assist with locating duplication, people who work with data should always research the possibilities and read reviews from users before picking options for their needs.

3. Apply Human Insights to Any Data Deduplication Tool

Tools exist that make it easier for marketers to remove duplicates from their databases. But, they should not become overly reliant on those solutions. For example, if a platform shows an entry of a duplicate address, users should not automatically regard it as an error.

For example, an address for an apartment in a college town could have a different valid occupant for each semester or even less often. Alternatively, there could be cases where two people from the same household sign up for the same service but have different subscriptions because they chose different feature tiers.

When data analysts use any interfaces that assist with removing duplicate data, they must take a closer look before deleting information that seems redundant. Even the best tech suites for controlling duplication can’t view the data with the context that a human can.

4. Change Duplication Removal Methods As Needs Dictate

Excellent data deduplication needs a methodical approach. Once marketers and data analysts come up with a method for removing duplicates, they need to log every step of the process and make a note of when each one happens. Otherwise, it’ll be impossible to keep track of what works and what doesn’t. It’s also best to test a deduplication process in a sandboxed environment before moving it to production.

Then, even after a deduplication method seems ideal, companies should still be open to changing it as necessary. For example, if an enterprise links another source of customer data to a marketing tool, that action could cause unwanted duplication. Then, the presence of another new marketing tool makes it necessary to change a previously successful deduplication process.

5. Prompt People to Avoid Signing Up Twice

Some duplicate data happens because customers can’t remember registering at a website before. So, one simple data deduplication marketers and data analysts can use is to ensure that sign-up forms urge existing users to log into their accounts instead of registering again.

Start Tackling Duplicated Data

Data duplicates are common, but not impossible to reduce. The steps suggested here can help data experts get to the bottom of duplication problems and start to solve them.

The post 5 Data Deduplication Strategies for Marketers appeared first on Reflective Data.

As you’ve likely heard before, a tool is only as good as the hand that wields it. If you’re unaware of the full range of capabilities and features your platform provides, you’re falling short of its full potential. More than that, you’re prone to errors that could compromise your marketing performance.

With this in mind, what are some of the most common analytical mistakes marketing professionals make? It’s wise to understand the pitfalls of analytics platforms, so you can avoid them with ease.

1. Choosing Favorite Tools

While some tools are more effective than others, you’ll never find a one-size-fits-all solution. No algorithm applies to every situation, and no data source provides all the information you need. You might prioritize certain features of your platforms over others, but it’s crucial to experiment and explore.

Some of the features you’ve overlooked might have incredible potential, but you won’t know until you’ve examined them. When marketing professionals play favorites with tools, they waste money and increase turnaround time. You’ll avoid these common mistakes as long as you keep an open mind.

When you’re developing your marketing strategy, try both new and old things, searching for solutions that are appropriate for your set of circumstances. These circumstances will change, and you have to evolve with them by adapting your approach. Dependence on any one tool is counterintuitive.

2. Picking the Wrong Graphs

You want to communicate your results to your team through data visualization, but it isn’t such a simple task. You might confuse them if you choose the wrong type of chart or graph, leaving them with more questions than answers. You have to represent different kinds of information in different ways.

As an example, line and bar graphs are best for showing progress over a period of time. Pie graphs are ideal for displaying the number of people in your customer base who come from various geographies. When deciding on data visualization, ask yourself what you want to achieve with a chart or graph.

When you look at data visualization within the context of your marketing goals, you’ll have a better understanding of what information is important and how to show it. Start with your initial intention for the data to sidestep some of the mistakes others in your position have made in the past.

3. Starting With Metrics

While many companies use platforms for collecting, storing and analyzing data, not all of them follow a coherent plan. In truth, it isn’t enough to have access to information. Once you obtain the data, you need to approach it with a clear and agreed-upon goal that your team understands.

It’s one of the mistakes startups often make with data, failing to set strategic priorities early in the process. You integrate something new into your website — like a favoriting feature — but you haven’t defined your goal. Without that goal, you don’t know what success looks like, or whether you’ve achieved it.

Amanda Richardson, chief strategy officer for the travel company HotelTonight, poses the important question: “…what’s the key metric that’s going to determine success?” Richardson goes on to explain: “Without that clarity, you end up with a situation where one person is saying, ‘That was great for our most popular users, who favorite an average of 12 hotels’ and another is saying ‘But this was intended for new users.’ And you’re thinking, ‘Was it?'”

Improve Your Marketing Strategy

A tool is only as good as the hand that wields it, and now you know how to use yours a little better. As long as you avoid these three mistakes, you’ll make the most of the platforms you employ every day and improve your marketing strategy.

The post 3 Analytical Mistakes You’re Making and How to Avoid Them appeared first on Reflective Data.

At Reflective Data, when we start working with a new client, we always start by figuring out what their current system consists of — and in many cases, it’s a real headache. Not to mention, when we ask the client about a specific tag or goal that they set up 6 months ago, they don’t remember anything — and the name they chose isn’t helping much either.

For several clients, we’ve really pushed the need for getting a proper naming structure across the company. And while it hasn’t always been easy, we’ve managed to fix it for almost all of them. And according to our clients, the new naming conventions made their work so much more effective.

Renaming everything is one thing but, unfortunately, it won’t provide a long-term solution. What’s needed is nomenclature guidelines and educating the team on how to use them, and the importance of it.

Some (bad) real-life examples from Google Tag Manager that we’ve seen recently:

All from a single setup with over 170 tags

1. Sending Google Pay click event
2. jquery-scrolldepth
3. UA event tracking for checkout
4. Hotjar for blog
5. Live chat widget
6. Conversion tag

Now, if your tag manager has 10 tags you could probably get away with names like this. But when you start adding more tags and/or if more than one person is working with the setup, things start getting messy.

So, how should you name your tags? Let’s start by how we renamed the very same tags.

1. GA – Event – Click – Google Pay
2. JS – Event – Scroll Depth
3. GA – Event – Clicks – Checkout
4. HJ – Main Snippet – Blog
5. Olark – Main Snippet
6. AdWords – Conversion – Purchase

There are several ways how you could approach naming your tags (or anything else) but we generally like the space-dash-space method. The key is to pick one and stick to it. Of course, formatting alone just the facade.

Here are a few guidelines that will hopefully help you a bit further:

1. Always have a documentation

So you are working on a new nomenclature for your business. That’s great, but not everyone might understand things the way you do. Even if they look at a few examples they might come up with something totally different. That is why you must have documentation for explaining it.

2. Educate your team

This goes with the previous point. You need to inform your people about the new nomenclature and explain to them how it works and why it’s really important. You must also make sure everyone is actually following the new rules and friendly remind them every time they don’t. Trust me, in the beginning, you will have to do it guide often.

3. Keep your names short

Include as little as possible but as much as needed. It is okay to use shortenings and acronyms (you should include them in the docs, though) and lose the words that are not absolutely needed.

4. Consistency is really important

Make a plan and stick to it. If you are using an acronym, always use the same acronym. Don’t change the order of the elements in your names.

• GA – Event – Click – Add to Cart
• GA – Event – Scroll Depth
• Event – Blog Comment – GA
• UA – Mobile menu click

5. Add some context

Make sure people looking at your names would understand what they are, what are they doing and where are they doing it. For Google Analytics events, you might want to have “GA” and “Event” in your name, followed by what kind of event it is and on which pages this event is coming from.

I hope these you find these guidelines helpful and they get you a step closer to having proper naming conventions at your company, too.

As a bonus, I’ve added some examples that I think are pretty good. Divided by where they’re used.

Google Tag Manager Triggers

• PV – Homepage (PV stands for page view)
• Click – Outbound Link
• CE – VWO – Data Push (CE stands for custom event)

Google Tag Manager Variables

• VWO – Experiment ID
• EC – Transaction ID (EC stands for enhanced ecommerce)

Google Analytics Goals

• Click – Complete Purchase
• PV – Newsletter Signup Complete (PV stands for page view)
• Event – Submit Comment – Blog

A/B Testing Experiments

• RD – Homepage – Benefits Bar – Desktop (RD means this test was built by Reflective Data)
• Sitewide – Change Main Menu Order – Desktop & Mobile

Do you have any further ideas for improving analytics nomenclature? Share them in the comments below!

The post Good Nomenclature Is More Important Than You Probably Think appeared first on Reflective Data.