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If you are a PPC (pay-per-click) manager, understanding how to analyze your campaigns’ data properly is crucial to success. In today’s online marketing world, data is the new gold.
Without parsing what is relevant and not, and how to apply it to your business, you could miss huge opportunities for growth through your marketing. However, the question of which metrics are important and how to analyze them is a difficult one.
In this blog, you will see there are two ways to perform the analysis one is to simple and focuses on single dimension to analyze and other is deep and involves different angles to analyze. You would understand which method will work best for your campaign regarding keeping track of your PPC metrics. That way, you can continually improve the conversion in your campaigns. Let’s get started:
The Cambridge Dictionary of Philosophy describes analysis as “The process of breaking up a concept, proposition, linguistic complex, or fact into its simple or ultimate constituents.”
Analysis is an activity for which one can say that “It has room for improvement.” While performing analysis, analysts are always confronted with the question of what to include and what not to include. In other words, what is enough without sinking too much time in it?
When approaching analytics, you need to take a systematic approach. Analysis takes raw data and presents it in a way that leads to insight for managers and key stakeholders. That way, their businesses can perform better.
Analysis is defined as partitioning a whole into parts or components, whereas analytics is a logical analysis method that is mostly computational. Data Analytics, other than analyzing data, also covers data collection, storage and organization, and the tools and techniques used to do all these. Analysis is done on the things that already exist, so it focuses on past and present, whereas analytics also covers the future.
What makes a successful analysis versus unsuccessful analysis? Let’s look at an example. If a store’s sales data is analyzed and a product has 1,000 units, all of which are sold, it would be considered a very hot and beneficial product for the store. But if the same product yields a negative return on investment for the store, it would then be considered a loss to the store.
If we look at it from another dimension, we might see that the product may have taken ages to sell out completely. So the success story from one angle could be a total disaster from another. Then how do we define success and failure, or in other words, how do we perform analysis to ensure we are looking at it from the right angles? We will address this in the subsequent sections.
Businesses need to extract data promptly, but you need to have clear objectives first to know what you’re looking for. Knowing the different analytics types helps you have a guiding light in your organization to be more effective in your analysis.
There are four broad categories of analytics to keep in mind. They cover everything from the operations, to planning, to ongoing improvement within your business. They support each other and do not replace the other. Each one is particularly useful for a specific part of your business:
In this type of analytics, a business evaluates what is happening in-house or in the field. Managers often use this, and supervisors to keep track of their teams, processes, and other areas of interest.
In essence, descriptive analytics provides a snapshot of the business in its current state. You can see what kind of previous successes or failures you had and learn from them for future growth.
Descriptive analysis is also referred to as the Business Intelligence of Advanced Analytics. It involves historical raw data collection, collects it, and then presents it to decision-makers so they can apply it toward business objectives.
This includes product sales, consumer behavior, and geographical areas that are performing well. This can come in the form of page views, likes, and other digital PPC metrics.
Gartner describes descriptive analytics as “a form of advanced analytics that examines data or content to answer the question — Why did it happen?” So descriptive analytics is focused on what, and diagnostic analytics is focused on the how part of it.
One technique is to do data drill-downs. Drill downs don’t require complex analytics but work on the basic aggregations and drilling down to a smaller level. These are generally performed by interactive visualizations used in dashboards.
Diagnostic Analytics is also referred to as “Root Cause Analysis,” as it used to look at the data more in-depth to understand the causes of events and behaviors. Therefore, hidden relationships between different data entities are unearthed.
Modern tools not only provide descriptive analytics but also try to attempt to provide diagnostic analytics. The inclusion of machine learning teams with domain experts helps improve the diagnosis and offers deeper correlations than those extracted manually.
Market competition has pushed businesses to think into the future and make decisions about events happening in the future. These decisions are based on Forecasting.
It is all about estimating the likelihood of an event happening in the future. These forecasting systems are based on forecasting models extracted from the historical data by extracting and analyzing the business’s past trends and behaviors.
The elements or variables included in the model provide individual inputs to give a cumulative model score that is then rated against a model prediction range to say if something is likely going to happen or not.
Predictive Analytics is used in businesses and various fields like rain and flood prediction systems, health monitoring systems, sports, and games, etc. However, we must remember that analytics systems are not future tellers. Dr. Michael Wu of Lithium Technologies explains:
“The purpose of predictive analytics is not to tell you what will happen in the future. It cannot do that. No analytics can do that. Predictive analytics can only forecast what might happen in the future because all predictive analytics are probabilistic in nature.”
The last king of analytics is more complex. It sometimes uses hybrid data (structured and unstructured data) and use the power of machine learning to make decisions and generate results. From here, it provides insights into what actions should be taken to maximize business objectives.
It is often used in large enterprises thanks to its complexity and resource consumption. For instance, it is used in the supply chain process to optimize delivery time. If you are having some online store and want to keep reasonable prices of products with profits based on your previous data along with predictive analysis, this prescriptive analytics can help you do this by automatically suggesting you prices and availability based on numerous factors, including customer demand, product variations most selling items etc.
Data is the source of intelligent decision making for any business. As the volume of data increases, so does the effort and complexity. That’s why Big Data, or deep analytics, is so useful. It takes this unstructured data and turns into something you can put to good use.
Deep analytics uses detailed processes, data mining, and organization techniques to look at large data sets and make sense, and move around the complexities. It can take everyday data from a granular level and extrapolate it so that you can see how it affects the business from a higher point of view. It’s a core part of business intelligence in today’s marketing world.
Deep Analytics involves feedback processes that get mature over time. Some deep data analysis is simple aggregation, whereas others need many cycles of processing to reach some meaningful, intelligent data finally.
The problem with deep analytics is that every piece of data is different, so it needs different treatment in terms of techniques and algorithms that need to be applied to extract useful information.
For example, clustering two attributes of population, income, and education, provide the geographical spread based on these attributes and makes sense in terms of census data. Simultaneously, clustering of blood group and sugar level may not provide useful information for a particular disease for pediatricians. They may have to do clustering of each of these attributes with some other attributes, or clustering may not work for them at all.
Traditional analytics has predefined algorithms. AI (Artificial Intelligence) introduced intelligent algorithms that can change and adapt to new data. It can process data almost as well as humans.
By having a large amount of input, it starts to learn. From here, it will give weight to specific PPC metrics and other data points to varying degrees. There are various levels in the system design. These layers create a pattern that acts as a backbone to deep analytics. The algorithm then goes to work on these to make multiple passes over the data and come out with new insights and connections each time.
Unlike traditional analytics, deep analytics is not restricted to individual data points or dimensions. And it isn’t limited by the narrow directives of traditional algorithms. The ability to learn and improve makes the analytics from this type of data even more powerful and useful.
The model becomes trained, and it molds itself to find the kind of data that you created it for in the first place. Each layer gets trained for the previous layer. In essence, you have a virtual machine that is always getting smarter and evolves in pattern recognition.
These newer algorithms don’t have to follow a linear pattern. They can find hidden data, and they do it automatically, reducing the need for further engineering. When teaming up with technology experts, let them know the kind of features you’d like to have included. If it is baked in from the start, it is much easier to achieve the type of data insights you’re striving for.
The kind of data we are dealing with today has grown far beyond the PPC metrics of the past. Whereas before you were coping with gigabytes, today there are petabytes and even exabytes of data. It is impossible for a human being to analyze this on their own. But deep analytics can explore hundreds of layers without any issue.
With transfer learning, the data scientists don’t need to develop a deep learning network from scratch. Most applications make use of existing trained models and then tweak them according to the domain and data. This is another aspect of carrying on the knowledge that reduces the time to reach the required system and application.
The most important aspect is that deep analytics enable organizations to work on large datasets that contain numerous dimensions and measures. This ability to tackle a large number of features is what elevates deep analytics from traditional analytics, and even surface-level machine learning analytics. This becomes even more beneficial when it is applied to large volumes of unstructured or text data.
Today’s marketer is facing the bombardment of data points, which makes it very difficult to evaluate the return on investment. The problem becomes compounded when a marketing manager runs multiple campaigns for numerous clients across various domains.
A marketing manager supported by a deep analytics system gets very deeply analyzed predictions and prescriptions that save time and boost ROI (return on investment) for clients.
There four primary objectives from which you can analyze a campaign:
With brand awareness, the main focus is getting the word out about your brand. You want your ads to display to as many prospects as possible. The PPC metrics you are looking at will be views, impressions, and potentially clicks.
If you are relatively unknown in your market, then this can be a solid plan to get started. Often, you lose out in the market not because you have an inferior product, but because no one knows about you.
Keep in mind that brand awareness takes some upfront investment. Sometimes, people need to see your ad more than once before they are interested or trust that you are legitimate.
Because of the time and upfront cost, it is often larger brands or those with robust budgets that aim for these campaigns. Still, if you are smart with your spending, you can achieve awareness without breaking the bank on a smaller budget.
While brand awareness is about getting more widely known, prospect engagement is the next step: actual engagement. Perhaps you already have some recognition in the marketplace. Or, based on your PPC metrics, you see that you’re having your ads seen and clicked on, but it’s time to get further engagement.
PPC metrics at this stage are often things like clicks, views, signup forms, and other activities that are trackable and represent an interest in your product or service. Prospect engagement is essential for certain sales cycles that take multiple steps or touchpoints.
For instance, a business consulting agency is more likely to look at smaller steps along the way – things like filling out forms, reading an article, scheduling a call, requesting a quote, and more. On the other hand, a brand that sells sneakers might jump into focusing on conversions:
Ultimately, PPC metrics can act as “vanity metrics” (those that don’t drive business results in a meaningful way) without being attached to conversions. Luckily, modern tools allow you to track your conversion from the channel that brought the customer to you, to the steps in between, to the sale.
A customer conversion can be a lead, but typically we are talking about sales. This type of campaign approach is only interested in generating revenue as quickly as possible. Everything else fuels this objective.
Of course, to increase your conversions, you can optimize each step of your funnel along the way–this includes your sales page and purchase calls-to-action (CTAs). The reason you may want to hold off on pure conversion-oriented campaigns is if your product has a longer sales cycle.
Once you have converted leads to customers, your job isn’t finished. It’s good to track PPC metrics that show how often people sign into an app, repurchase, or visit your site over time. This gives you an idea of how engaged people are with your business after the point of sale.
The real profit in PPC marketing is often made on the backend. In other words, the first purchase should help you break even on your marketing costs, but down the line, revenue will increase with future purchases by the same customer.
The reason is you only paid the marketing price once but are getting multiple sales. If you genuinely want to grow a long term business, don’t neglect retention as a core aspect of your PPC metric analysis.
The analysis gets even more complicated with each passing phase, as the previous phases also need to be evaluated with the currently ongoing phase. This makes the job of an analyst even more difficult. Whereas oversimplifying can cause catastrophic results. There needs to be a balance.
Getting the right results from PPC campaigns isn’t easy. It takes a lot of analytical skills and marketing savvy. However, those teams and managers who can do it well will see extraordinary growth in their online brands.
The key to PPC metrics is making sure that you are looking at the right things, often enough, without going overboard. Then, you can achieve a balance of effectiveness without losing efficiency.
Review the analytical tips above, and then select a few that will help you in your specific business right away. If you apply the principles daily, over time, you can see better conversions, lower costs, and will get greater clarity in your PPC campaigns.
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