![\"optimizing](\"https:\/\/www.ppcexpo.com\/blog\/wp-content\/uploads\/2019\/10\/optimizing-ppc-campaigns-with-data.png\")
<\/div>\nUnderstanding how to use all of this information and what exactly it can be used for is an exciting, but also challenging prospect.<\/p>\n
Big data can be overwhelming, to say the least.<\/p>\n
The purpose of this guide is to focus on how data can be used to optimize PPC campaigns<\/a> on Google Ads<\/a>. Some of the topics covered in this article include:<\/p>\n By definition, data analysis is a multi-step process with the end-goals of uncovering useful insights, drawing conclusions and finding compelling evidence to support decision-making. There are many different types of analysis and steps in the process, which we will explain in later sections of this guide.<\/p>\n In terms of PPC campaigns<\/a>, analyzing data is a necessary strategy. Advertisers have to monitor and analyze their performance metrics to understand the overall health of their campaigns and what adjustments should be made.<\/p>\n These metrics cover the major components of paid ads, but expert PPC advertisers<\/a> know that other data sources are equally valuable to the success of campaigns.<\/p>\n Sales data, industry benchmarks, customer interactions, it all matters. The more data sources you\u2019re able to pull from, the clearer the picture becomes.<\/p>\n With actionable insight into your customers and competitors, as well as the market as a whole, you can make course corrections for your PPC campaigns.<\/p>\n There are five different approaches you can take to performing PPC-related data analysis. The method you choose will be determined by your end-goal.<\/p>\n What do you hope to get out of the analysis?<\/p>\n Once you\u2019ve answered this question, the path to choosing the right approach will become clear.<\/p>\n It\u2019s worth mentioning that you\u2019ll use each approach for different projects. There is no best overall approach. Often, you\u2019ll start with one analysis strategy that creates questions that need a second or third approach to answer.<\/p>\n Let\u2019s break down each approach, and when you should use it.<\/p>\n Statistical analysis is a form of business intelligence<\/a> (BI) that uses numeric data.<\/p>\n PPC creates a lot of statistical data. One look at your campaign dashboards and you\u2019ll see a lot of numbers staring back at you.<\/p>\n These numbers give you the most current figures from your campaigns. But, by looking at more complete sets of these structured metrics, you can begin detecting trends that aren\u2019t visible on the surface.<\/p>\n These patterns in your statistical data can help you detect and even predict trends. Better yet, statistical data helps advertisers understand why these changes are occurring.<\/p>\n The statistical data analysis approach is great if your end goal is to understand and improve the overall performance of campaigns<\/a>.<\/p>\n This approach is also known as the \u201cdrill-down\u201d approach. It is designed to go deeper than say a statistical analysis.<\/p>\n The exploratory approach is much more focused and involves a single data set. It\u2019s an excellent approach for after you\u2019ve detected a possible trend in statistical analysis and want further evidence to support the correlation.<\/p>\n It is also a great strategy if you want to drill into a single metric, which can be very helpful to a PPC advertiser.<\/p>\n By intimately exploring your most important metrics (impression share<\/a>, CTR, cost-per-click, etc.), you can better understand what factors positively and negatively influence these figures. And, identify where potential problems in your campaigns exist.<\/p>\n In short, an exploratory data analysis<\/a> answers questions regarding a single set of data or a specific metric.<\/p>\n Any time you look at the relationship between two or more data sets, this is an associative analysis.<\/p>\n An associative analysis is something that all PPC advertisers and digital marketers use because marketing and advertising metrics<\/a> are so closely related to one another.<\/p>\n By exploring the relationship of two or more related data sets, it adds context to our understanding. We start to see the big picture behind our data and how each figure impacts the next.<\/p>\n When we can identify these relationships and sequences, it becomes easier to learn about audiences and the tactics that lead to higher ad performance. You can also see where hiccups in your campaigns are occurring.<\/p>\n For example, when we look at a campaign with a below-average conversion rate, there\u2019s several metrics that can be examined to find the cause. First, we may analyze clickthrough-rates<\/a>. Is the ad not converting because people are not clicking?<\/p>\n Then, we see that the CTR<\/a> is normal; we know people are clicking. Now we can surmise that the problem occurs after the click. So, we investigate bounce rates and find that people are not moving beyond the landing page.<\/p>\n Moving through this sequence helps us understand what\u2019s going on. That is an associative analysis.<\/p>\n Similar to statistical analysis, the historical analysis approach uses data to identify trends and patterns. The difference is that a historical data analysis looks at past performances, rather than your current ad statuses.<\/p>\n If you need any explanation for why this type of analysis is essential, you can open your history books to Churchill\u2019s famous address to the House of Commons. \u201cThose who fail to learn from history are condemned to repeat it.\u201d<\/p>\n Every PPC advertiser has made mistakes in the past. It may be something as small as bidding on negative keywords<\/a>. Or, a much bigger issue.<\/p>\n No matter the case, it is crucial to recognize your past campaign performance and use those positive and negative insights to steer your present and future strategies.<\/p>\n After all, you don\u2019t want to continue investing in bad keywords or irrelevant audiences!<\/p>\n Historical data analysis is a great approach to fact-check other insights. For example, you\u2019ve uncovered a possible relationship between two data sets. To test its accuracy, you can see if that relationship is a new trend, or something you can back with historical evidence.<\/p>\n If it is a new trend, then it is best to observe it carefully, before you know for sure that it is accurate. If you have ample historical data to back up the correlation, then it is much safer to say it is a concrete relationship.<\/p>\n These are the benefits of the historical analysis approach.<\/p>\n As the name suggests, comparative data analysis <\/a>is used when you are comparing two sets of data. Typically, these two sets of data are from two different timeframes.<\/p>\n By conducting a comparative analysis that looks at two points in time, you can see how performance has changed or been affected by the adjustments you\u2019ve made.<\/p>\n In other words, it can give the \u2018before\u2019 and \u2018after\u2019 comparison that is so vital towards judging if you\u2019ve made the right changes in your campaigns.<\/p>\n The comparative data analysis approach also gets brought up when you are studying competitors.<\/p>\n Conducting a comparative analysis between your strategies and your competitors\u2019 is vital. A competitor comparison analysis shows which keywords your opponents are targeting, what sort of ad headlines they are using and what success they are having.<\/p>\n You need to know what you\u2019re up against as this provides insight into how you can counter-strategize.<\/p>\n While on the topic of data analysis approaches, it is also important that we address qualitative versus quantitative data.<\/p>\n Quantitative data <\/strong>is abundant in PPC. Anything that can be counted or measured by a number is quantitative data \u2014 quantities. It\u2019s raw, structured data. Clicks, impressions, conversions, ad spend<\/a>, CTR, bounce, session durations, these are all quantitative data points.<\/p>\n Qualitative data, <\/strong>on the other hand, is unstructured or semi-structured. It’s not measured by numbers, but rather categorical properties, identifiers or other qualities. We use qualitative data particularly when grouping keywords into ad groups or segmenting audiences.<\/p>\n Qualitative data also appears in areas like ad relevance, landing page experience and expected CTR, where Google assigns a below average, average or above-average rating.<\/p>\n The line blurs when <\/strong>we look at quality score<\/a>. Google uses a lot of qualitative data to determine your quality score, even though the result is represented as a quantitative number. This is a perfect example of how both types of data work together.<\/p>\n Often, we see qualitative data being used to theorize about possible correlations, trends or conclusions. Then, quantitative information is used to provide structured evidence to back up your qualitative claims.<\/p>\n Understanding the relationship between these two data types is key to any successful PPC analysis.<\/p>\n We\u2019ll explore how to perform a PPC audit<\/a> with your data in the final section of this article. For now, let\u2019s look at a basic overview of the five stages of the data analysis process:<\/p>\n Heading into a data analysis without a good plan is damaging not only to the analysis, but also your ability to interpret and utilize the findings.<\/p>\n Analyzing data for the sake of analyzing data is a waste of resources and won\u2019t yield the accurate, valuable insights that drive better decisions and deeper understanding.<\/p>\n So, our very first step is to have a goal. Typically, our goal is to use data to answer a question. The goal itself may be in the form of a question. For example: \u201cHow can we acquire more conversions without increasing ad spend?\u201d<\/p>\n It\u2019s also important to align this goal to your overarching organizational objectives. Setting a goal for the sake of setting a goal is the same as analyzing data just to analyze data.<\/p>\n You want to answer a question that helps further your progress towards achieving that overarching objective.<\/p>\n Once we have a question to guide our analysis, we can begin choosing the relevant data sources.<\/p>\n On the surface, gathering this data seems simple. And, sometimes it is. Other times, however, it is much less so.<\/p>\n The issue at hand is that we want the least amount of data to achieve our analysis goal. Extra data will often make it harder to derive insights and can even lead to inaccuracies and incorrect conclusions.<\/p>\n If we can achieve the desired outcome using two sources of data, then we use two sources of data. Later, when we ask follow-up analysis questions, then we can explore those other sources.<\/p>\n Keeping your data sources at a bare minimum becomes a challenge when we have metrics that are very closely related to one another. This is common in PPC.<\/p>\n For example, impressions versus impression share, revenue versus profits, conversion value versus value per conversion \u2014 each pair of terms is very similar to one another. This creates confusion when you\u2019re trying to decide which options are most relevant to your analysis.<\/p>\n We\u2019ve collected the right data sources and now we\u2019re almost ready to start the analysis. But, before we can analyze the data, we need to make sure that each unique data source is clean.<\/p>\n Clean data sets are complete and errorless. If an inaccuracy goes unchecked and uncleaned, it can corrupt your entire analysis!<\/p>\n There are several traits that your cleaned data should possess:<\/p>\n Uniformity ensures that unalike data sources can \u201ctalk\u201d to one another, which is necessary for accurate analysis.<\/p>\n This is a big challenge, especially if you are dealing with both unstructured and structured data<\/a>.<\/p>\n Analyzing data is both an art and a science.<\/p>\n The art is determining the right way(s) to manipulate data to create the intended results. The science part involves identifying, calculating and comprehending the various trends, correlations, outliers and other relationships.<\/p>\n Most data scientists will start with a pivot table. This allows you to filter your data based on mean, max, min, standard deviation and other variables. Alternatively, you can plot the data in a chart and find correlations that way.<\/p>\n It\u2019s possible that the data you\u2019ve gathered isn\u2019t the right data, or that your initial question changes slightly. In these scenarios, you may have to go back to steps two and three and introduce additional, new sources of data.<\/p>\n In a way, there\u2019s no wrong way to manipulate your data. Any fresh angle or lens you can look at the information through can yield previously undiscovered results.<\/p>\n There is, however, a right way to manipulate your data that will ultimately provide the answers you\u2019re looking for.<\/p>\n With your analysis completed, the task shifts to reporting and visualizing your findings. This is the stage where we interpret the results and find ways to showcase those findings to ourselves and others.<\/p>\n The extent of our need to report and visualize is highly dependent on the scope of the analysis.<\/p>\n For example, if we\u2019re conducting a basic analysis for ourselves, a simple report suffices. This report could be a brief summary of the findings and whether or not your original question or hypothesis has been answered or confirmed.<\/p>\n When you need to showcase your findings to other people, however, a more detailed report is required. This report will not only detail the findings, but also break down the entire analysis process, highlight potential limitations of the results and any follow-up research that still needs to be conducted.<\/p>\n Often, data reports will include tables, graphics and graphs that help visually represent the results and tell the story of the process that went into reaching the achieved results.<\/p>\n This is known as data visualization<\/a>.<\/p>\n Visualization is particularly important when showing analysis results to stakeholders that may not have a mind for analytics. Visually representing the data may be your only hope for them to understand the insights you\u2019re conveying.<\/p>\n Sometimes, visualizing data in different ways can also reveal to you, the data scientist, insights that you couldn\u2019t see in your raw spreadsheets.<\/p>\n Data analysis is unmistakably complex. As your head is swimming inside a fishbowl of numbers, metrics and other kernels of data, you may start wondering, what in the world am I doing all this for?<\/p>\n Well, because you want your PPC campaigns<\/a> to improve, right? Data illuminates the path between where your PPC efforts are, and where you want them to be.<\/p>\n Improving your PPC campaigns and brightening this aforementioned pathway requires a deep understanding of where your wins and losses occur.<\/p>\n At the heart of these wins and loses is optimizing keyword bids by performance. Specifically, which keywords are paying off and deserve more attention, and which you need to toss on your negative keyword list.<\/p>\n This is not an easy task because even a small PPC campaign can consist of hundreds of individual keywords. And, each one is influenced by a number of different factors, like user behaviors, competitor strategies and changes to the platform.<\/p>\n Individually studying each keyword requires a lot of tiresome work, without much result. You want to work smarter, not harder, and that\u2019s the edge that data analysis provides.<\/p>\n By analyzing your keywords for trends and changes, you can better identify terms that are acting abnormal and make the necessary adjustments quickly.<\/p>\n Data analysis also lifts the veil on understanding why these changes are occurring. Is it just a one-off anomaly? Or, does this outlying change reflect a more significant trend that you can take advantage of?<\/p>\n When we talk about PPC keyword<\/a> wins and loses, we\u2019re not just talking about performance. We\u2019re also talking money.<\/p>\n You want to make the most of your ad budget, especially if you\u2019re a smaller business that doesn\u2019t have the deep wallet of your competitors.<\/p>\n This is a secondary benefit of optimizing keywords<\/a>. When you protect yourself against negative or underperforming terms, while doubling efforts on the words that net the most return, you\u2019re allocating your budget more appropriately.<\/p>\n Thus, you\u2019ll see better returns on your ad budget that will help you grow your business.<\/p>\n As you optimize your keywords, you\u2019ll detect search queries that have user intent behind them. For example, \u201cbuy a new bike\u201d suggests that the user is intending to buy a bicycle. If a potential customer searches for \u201cbest bikes,\u201d we don\u2019t have that same buying intent.<\/p>\n User intent is a very important part of PPC marketing because it allows us to target customers that are at the end of the funnel and ready to convert. Generating highly qualified leads<\/a> is a key advantage to PPC<\/a>.<\/p>\n By exploring your data for the terms that lead to conversion most often, you can begin to see what level of user intent existed for those searches. Is their intent aligned with what your ads offer? If not, it may be a sign that you need to highlight different offers, products or benefits in your ads.<\/p>\n Next on a PPC advertiser\u2019s priority list is audience segmentation. The better we can group our different audiences, the easier it becomes to understand their behaviors and how to approach them with effective PPC content.<\/p>\n Relevance is key in successful PPC<\/a>. If your ads aren\u2019t relevant, or valuable, your ad impressions don\u2019t click or convert. When we have broad audiences, it is hard to develop ad content that is relevant to everyone.<\/p>\n With deeper audience targeting, on the other hand, you\u2019re able to serve the direct needs of each small audience cluster.<\/p>\n Effective segmentation requires you to identify your audiences and analyze the tactics that work best. It\u2019s one part market research and one part of data science.<\/p>\n There\u2019s a lot of different ways you can segment audiences, such as by demographics, locations, behaviors, etc. These are unstructured and structured data points that you can use to conduct analysis projects.<\/p>\n Once you have your audience categories determined, you can use data analysis again to measure the success of your strategies within each segment. This breeds a more profound understanding of how each group behaves and where their interests and attitudes lie.<\/p>\n Pay per click advertising is competitive. You\u2019re always going to have other advertisers vying for the same top ad ranks and placements that you want.<\/p>\n Analyzing your competition has many benefits. First, it allows you to measure your place in the marketplace. Are you a big fish in a small pond, or the opposite?<\/p>\n Next, it gives you insights into how your competitors are navigating the same PPC landscape. In other words, what keywords they are targeting, what sort of landing pages<\/a> they are bringing to the table, how they structure their ads, what CTAs they prefer, etc.<\/p>\n Answering these questions creates a much clearer image of how you match up with your PPC competition. As mentioned, you can use the insights to effectively counter-strategize.<\/p>\n A successful competitor analysis can reveal opportunities or gaps in the competition where you can earn an advantage. This is a key benefit, especially when working against intense competition.<\/p>\n There are no inherent disadvantages to optimizing campaigns with data. It\u2019s a necessary step if you want to succeed in the PPC marketplace.<\/p>\n There are, however, some obstacles that you need to be aware of.<\/p>\n Other analysis obstacles come when dealing with large volumes of data.<\/p>\n While every source of data is valuable in its own right, conducting a successful data analysis is very much a matter of right time, right data.<\/p>\n As we mentioned earlier, it is important to only focus on the data streams relevant to the goal of that specific project.<\/p>\n Otherwise, you can run into issues of data fatigue.<\/p>\n Data fatigue is a relatively new term coined by analytics experts. It varies in definition, but the cause is always the same: too much data.<\/p>\n There are three common ways that data fatigue manifests itself:<\/p>\n 1.) Picking the right data sources: <\/strong>Today\u2019s businesses have a multitude of data sources. It\u2019s where we get the concept of \u2018big data.\u2019 As hinted above, there\u2019s a significant challenge for marketers to know which data sets work together and yield the right, relevant results.<\/p>\n When the wrong data sets are paired together, there are two main gigantic risks. First, irrelevant data sources in an analysis can get in the way of the accurate conclusions that your business desires. Second, and worst of all, when incorrect correlations between data sets are accepted as fact, businesses follow them deep into the rabbit hole, only to find a dead end.<\/p>\n 2.) Too much at once: <\/strong>Businesses try and procure the maximum amount of value by analyzing everything at the same time. Yet, they quickly become saturated in information to the point that they can barely figure out which insight to listen to first.<\/p>\n It\u2019s the classic child at Disney World syndrome. Everything is exciting all at once, but by the time you\u2019ve ridden Pirates of the Caribbean twice and took a photo with Mickey Mouse, Pluto and Donald Duck, and had a late-afternoon \u201cdinner\u201d consisting of an extra-large cola and cotton candy, that over-the-moon-excited feeling is quickly being surpassed by a cumbersome sugar crash.<\/p>\n The remedy? Take your time and don\u2019t let data overwhelm you!<\/p>\n 3.) Following data to a fault: <\/strong>Data is a potent tool that helps fuel better decision-making processes. But, some businesses become so driven by the data and analytics that they neglect the opinions and experiences of their staff. Not only does this undervalue your in-house experts, but it also chokes creativity. Sometimes, taking a creative risk can pay off huge! Also, becoming entirely data-driven requires a very deep understanding of data science and extremely sophisticated systems.<\/p>\n Now that we\u2019ve discussed the many details related to PPC data analysis, we can get into the how-to section. Since data analysis can include a wide range of projects, we\u2019ll look at a simple PPC account audit.<\/p>\n Auditing your PPC account means conducting a detailed and structured evaluation of your goals, campaigns, settings and other related components. The purpose of this audit is to find attention-worthy issues in the PPC\u00a0 account.<\/p>\n The ideal outcome is that identifying these attention-worthy items will lead to actionable insights towards improving your campaigns.<\/p>\n Advertisers spend a lot of time investigating their keywords, ad groups and other campaign details. What they often neglect is auditing their PPC goals.<\/p>\n Remember, this is the driving force behind your strategies. So, if your goal is off, everything that follows will also falter. It\u2019s like having a craving for Chinese food, but you accidentally drive to the Italian restaurant instead.<\/p>\n Here are some questions to consider when auditing your PPC goals:<\/p>\n With our goals reaffirmed (or adjusted), we can now audit our campaigns<\/a>. This is where we want to tighten the nuts and bolts of our campaigns and make any necessary changes to help produce better results moving forward.<\/p>\n Areas of concern for auditing your PPC campaigns include:<\/p>\n Now we move into the smaller details of our campaigns: keywords. We\u2019ve talked at length about the importance of managing keywords already. For our PPC audit, we\u2019ll evaluate the following questions:<\/p>\n Next to keywords, you also need to audit your bidding strategies<\/a>. Optimizing your bids means keeping your budget well allocated to produce the best returns.<\/p>\n You need to ask yourself the following:<\/p>\n Often, we set and forget our campaign settings. This is fine for the short-term, but, after a while, we need to review these options and make sure they still make sense for our campaigns. You may even be neglecting certain settings that are hurting your PPC ads!<\/p>\n Here are some questions to evaluate:<\/p>\n No matter how optimized your keywords<\/a>, settings and other components are, it\u2019s all for nothing if your ads aren\u2019t compelling. Bad ad copy doesn\u2019t draw attention, nor does it entice those vital clicks that you need to fuel conversions.<\/p>\n Here are the questions to explore in your ads audit:<\/p>\n While your landing pages are technically outside the realm of your Google Ads account<\/a>, they directly impact your metrics and campaign performance. Thus, it is essential to include them in your PPC audit.<\/p>\n Specifically, think about these questions:<\/p>\n As you perform more audits, you\u2019ll have a thorough record of your entire PPC strategy overtime. It\u2019s a good practice to evaluate your latest review based on previous benchmarks.<\/p>\n Is your CPA moving in a positive or negative direction after the last audit? This will help you see if the changes from your last audit had the desired effect. If they didn\u2019t, you can analyze further and see where the disconnect occurs.<\/p>\n The bottom line is this: you can\u2019t run successful PPC campaigns without listening to your data. PPC without data is like baking or cooking without measurements. You can do it, but the results won\u2019t be nearly as good.<\/p>\n We hope that this resource helps you better utilize your data to achieve optimal results in your pay per click advertising.<\/p>\n If you\u2019re struggling with conducting your own PPC audit, the PPCexpo Account Audit Report<\/a> tool can simplify the process and connect you to actionable insights quicker.<\/p>\n","protected":false},"excerpt":{"rendered":" Every company is producing massive volumes of data every second of every day. This trend is heavily influenced by the many new channels created by the Internet.<\/p>\n <\/a><\/p>","protected":false},"author":1,"featured_media":6358,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[785],"tags":[276],"_links":{"self":[{"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/posts\/6357"}],"collection":[{"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/comments?post=6357"}],"version-history":[{"count":3,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/posts\/6357\/revisions"}],"predecessor-version":[{"id":43015,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/posts\/6357\/revisions\/43015"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/media\/6358"}],"wp:attachment":[{"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/media?parent=6357"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/categories?post=6357"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ppcexpo.com\/blog\/wp-json\/wp\/v2\/tags?post=6357"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
![\"ppc-signal-optimization-tool\"](\"https:\/\/ppcexpo.com\/blog\/wp-content\/uploads\/2021\/12\/ppc-signal-ad-3.png\")
<\/a><\/div>\nWhat is Analysis And Why is It Important?<\/h2>\n
Types Of PPC Data Analyses<\/h2>\n
Statistical Analysis Approach<\/h3>\n
Exploratory Analysis Approach<\/h3>\n
Associative Analysis Approach<\/h3>\n
Historical Analysis Approach<\/h3>\n
Comparative Analysis Approach<\/h3>\n
Qualitative Versus Quantitative Data<\/h2>\n
The Analysis Process<\/h2>\n
\n
Stage 1: Planning<\/h3>\n
Stage 2: Gathering \/ Mining<\/h3>\n
Stage 3: Filtering \/ Cleaning<\/h3>\n
\n
Stage 4: Analyzing<\/h3>\n
Stage 5: Reporting \/ Visualizing<\/h3>\n
The Benefits Of Optimizing PPC Campaigns With Data<\/h2>\n
Identify Positive And Negative Changes In Keywords<\/h3>\n
<\/div>\n
Optimize Ad Spend<\/h3>\n
Leverage User Intent<\/h3>\n
Segment Audiences More Effectively<\/h3>\n
Measure The Competition<\/h3>\n
Obstacles When Optimizing PPC Campaigns With Data<\/h2>\n
\n
Beware Of Fatigue And Other Data-Related Obstacles<\/h2>\n
How To Perform An Effective PPC Account Audit?<\/h2>\n
What Is A PPC Account Audit?<\/h3>\n
Step 1: Goals Audit<\/h3>\n
\n
Step 2: Campaign Audit<\/h3>\n
\n
Step 3: Keywords Audit<\/h3>\n
\n
Step 4: Bid & Budget Audit<\/h3>\n
\n
Step 5: Settings Audit<\/h3>\n
\n
Step 6: Ads Audit<\/h3>\n
\n
Step 7: Landing Page Audit<\/h3>\n
\n
Step 8: Compare To Past Audits!<\/h3>\n
<\/div>\n
Conclusions:<\/h2>\n