Nov 18, 2022 6389 reads

10 Google Sheets Tips That Will Make Your Life Easier

Google Sheets (formerly Spreadsheets), a fully integrated product of Google, is used today in many fields and for many tasks. The widespread use of this tool is due to the usefulness it offers to its users, its compatibility with other tools and programs, and its many functions. In this article, we have given tips on how to use the Google Sheets tool more effectively and efficiently.What Is Google Sheets?Google Sheets is undoubtedly one of the most important programs that make our lives easier, especially if you work in a company that uses Google tools or in an industry where you are constantly interested in dynamic data. Sheets, which is also compatible with Microsoft Excel, is one of the most important parts of Google Docs. It is a type of Spreadsheet with numerous services such as editing complex data, and the possibility of common use thanks to its online-based nature.History of Google SheetsGoogle Sheets is from XL2Web, a web-based spreadsheet application developed by 2Web Technologies, founded by Jonathan Rochelle and Farzad "Fuzzy" Khosrowshahi. XL2Web was acquired by Google in 2006 and turned into Google Labs Spreadsheets. On June 6, 2006, a test version was released for a limited number of users on a first come, first served basis. The limited testing was later replaced with a beta version that was made available to all Google Account holders at about the same time as the release of an official announcement press release.In March 2010, Google acquired the online document collaboration company DocVerse. DocVerse allowed multi-user online collaboration on Excel-compatible documents as well as other Microsoft Office formats such as Microsoft Word and Microsoft PowerPoint. Improvements based on DocVerse were announced and deployed in April 2010. In June 2012, Google acquired Quickoffice, a free proprietary productivity suite for mobile devices. In October 2012, Google Spreadsheets was renamed Google Sheets.If that much general culture is enough, let's get to the tips.10 Google Sheets TipsGoogle Sheets, which allows performing many operations from simple to complex, has many different functions. While some of these functions are very often used and widely known, some very useful functions are not known to everyone. These tips will come in handy to help you use Google Sheets the way it works best for you.1. Dynamic ReportsUndoubtedly, the biggest convenience and the reason why Google Sheets is preferred is that it provides multi-use at the same time thanks to its online-based nature, and dynamic reports and dashboards can be created with internet-connected data sources. Google Sheets is for you, especially if it is of great importance to constantly monitor metrics, such as digital marketing, hourly, maybe minute by minute.If what you've read so far hasn't lit a light bulb in your mind, let's think together. If you have 3 different constantly updated data sources in your hand, if it is important for you to be able to follow these sources instantly and you have to keep track of these files that you have downloaded in different formats on your computer, you can combine these sources in one file thanks to Google Sheets and access their sources with the right methods (more on this one by one in a moment). You can get self-updated reports that you can follow forever, at once.2. Add-onsAdd-ons... In their simplest form, they are user-created scripts that run within Google Sheets, Docs, Slides, and Forms. With these mini scripts, you can get some data you want from free APIs, you can easily do many tasks that you have difficulty doing manually with one click, and you can set up an automation that will rock developers without requiring any coding knowledge.In order to access the add-ons, you can click on the Extensions menu in the top menu and then the Get add-ons section, and select the add-on that suits your needs.Caution: Installing unreliable add-ons can be dangerous for the security of your data.3. importrange, importxml, importhtmlImportrange, Importhtml, Importxml, and more. As their names suggest, these three functions and similar ones allow us to transfer any data we need to Sheet files, stock market data, prices on shopping sites, data in another file, and countless data you can think of. In this article, I will not explain the uses of these functions, but if you want, you can review them from the links below and perform your data import operations according to your needs.Importrange Importxml Importhtml4. Apps ScriptsApps scripts is a service where you can make all Google-based programs talk to each other, set up unlimited automation, create your own add-ons, and create functions with a little JS knowledge.Again, Apps Scripts, which you can access from the same section with add-ons, reveal a world where we can handle the work that we have been dealing with for hours, maybe in a few seconds.If you are interested, you can start developing excellent projects by reading Google's documentation right away.A little tip: Google apps scripts have a limited run time of 6 minutes, with paid accounts it can go up to 30 minutes. You should pay attention to the working time and efficiency of the projects you create.5. Co-editingWe can say that the most important feature of Google Sheets, which distinguishes it from Microsoft Excel, is co-editing. You can edit the fields that different employees are responsible for on different pages of a table at the same time, inform your teammates with the comments you add, and even solve your instant communication problem from the chat area offered by Google while in the file.In addition, you can control who viewed the file you shared from the Document History section and how often, and you can set deadlines accordingly.6. Google ToolsOne of the biggest benefits of Google's providing alternatives to meet almost all of the most used programs in business life is to be able to make these applications talk to each other and automate many processes easily. For example, let's say you update data from the same tables to the same presentations every week. Thanks to the Google Sheets and Google Slide connection, you can automatically update your presentation when you just update the table, by connecting the tables you need to the appropriate Slide tables and get rid of extra workloads.Or, you can send the data sheets that you regularly mail to as often as you want by connecting to Google Sheets - Gmail without having to deal with it over and over again every day.In short, you can have all Google's tools talk to each other, facilitate the processes you deal with from place to place, and use your time much better.7. VlookupVlookup is perhaps one of the most important Google Sheets functions that allow us to match data in different rows in two different data tables. For example, you have user_id data and session metric in one table, and label information corresponding to these ids in the other table and you want to match them. For a file with thousands of rows, it may take days to do this manually! With vlookup it will take 15-20 seconds.=VLOOKUP(search_key, range, index, [is_sorted])For more detailed information: Vlookup8. QueryLet's come to the most crucial point of the article and my clear favorite function: Query. With this function, with a syntax similar to the SQL language to organize and report your datasets, you can do things that are really complex and much easier than you can do using the & feature of Sheets 4-5.Syntax =QUERY(data, query, [headers])As you can see, let's write a small example formula of how we can use this function with only 2 mandatory parameters. Let's say the operation we want to do is as follows: By examining columns A, B, C on the "Data" page, we want to select the numbers in column A that are greater than 10 and also the cities in column C that are "Ankara", and the results of this query are larger than column B. Let's sort them in descending order. =QUERY(Data!A:C,"SELECT A,B,C WHERE A>10 AND C<>'Ankara' ORDER BY B DESC",1)9. IFSIf, sumif, countif, maxifs, minifs, dateif, iferror... As it can be understood from the if pattern, functions with ifs allow us to perform operations on most conditional situations. At the same time, it is very important to know what we need to be a good Sheet user and to practice using it. . That's why we need to know what functions with ifs are and how they're used.Let's look at a small if example together: We want to distribute letter grades such as A,B,C,D according to the scales of grades received by students in a class, then simply:=IFS(A3>89.99,"A",A3>79.99,"B",A3>69.99,"C",A3>59.99,"D",A3>0,"F")Or, if we want to aggregate sessions in column B where the sources in column A are "google":=SUMIF(A:A,"google",B:B) For more detailed information, you can review the contents in the links below. Sumif If CountifMaxifs Minifs10. MacrosFinally, Macros, one of the most important features of Sheets, which we can define as a lifesaver, especially for users who are tired of repeating the same manual operations every day. Macros, which can also be accessed from the extensions section, is an application area where you can repeat the UI operations you will save as often as you want, and it works based on Scripts. In fact, to summarize simply, it allows you to perform operations such as copy-paste and insert rows that you do every day in the format you specify, and has numerous uses. From Google's developer document, you can examine the usage areas, record your macro according to your own needs, and automate the processes you spend hours every day in a few seconds. You can access the Macros document here.In addition, from the Google function list page, you can review all functions, find the ones you need, and expand their usage areas.

Oct 21, 2022 5561 reads

What Are Performance Marketing Metrics? The 15 Most Commonly Used Metrics

Marketing metrics play a crucial role in interpreting the campaigns we launch and the ads we create. It’s essential to derive insights from the data we have and take action accordingly. Accurately analyzing a campaign’s metrics and taking the most effective actions during this process is critical.Misinterpreting performance success—whether through missing or incorrect analysis—can lead to mistakes in judging whether our campaign is performing well, causing both short- and long-term missteps. In this article, we’ll share tips on how to correctly interpret the most frequently tracked and most important metrics in performance marketing.What Does “Metric” Mean in Performance Marketing?Marketing teams use measurable figures called marketing metrics to show the success of campaigns and projects across all channels. Examples include Conversion, CTR, and ROAS.What Is a Conversion?When creating a digital marketing campaign, every brand has one or more goals that motivate users to take action. All marketing efforts are ultimately aimed at achieving these goals.A Conversion is the metric that counts how many times a specified goal is achieved within a website, page, or mobile app. For an e-commerce company, this metric might be the number of sales; for a consulting firm, the number of leads (form submissions). A high conversion rate indicates your ad campaign is successful and your landing page content is compelling.If your conversion count is low, you might improve it by changing site content, creating new funnels to simplify the conversion process, or updating offers to provide a more effective experience for users.What Is Conversion Rate?Conversion Rate measures how often conversions occur as a result of your ads. It shows the percentage of ad interactions that turn into your defined conversion goal. By looking at conversion rate, you can optimize landing pages, calls to action, content, and design, and gauge how compelling your ads are to your target audience. Conversion Rate is calculated by dividing the number of conversions by the total number of sessions. For example, if an ad generates 300 sessions and 10 conversions, the conversion rate is 3%.What Is Conversion Value?Conversion Value assigns a numerical value to specific conversions in your campaigns, helping you measure how valuable those conversions are to your business. By assigning different values to different conversions, you can make your campaign’s value measurement more meaningful. You can compute Conversion Value using the formula: Sessions × CR × Average Order Value.For example, if a shoe retailer has 10,000 sessions, a 1% conversion rate, and an average order value of 1,000 TL, the Conversion Value is 100,000 TL.What Is CPC?CPC (Cost Per Click) simply shows how much you pay for each click. Because you pay per click, irrelevant users clicking on your ads increase your unnecessary costs and raise your total spend.CPC is one of the key metrics for evaluating campaign performance. To lower CPC, you can optimize ad relevance, switch bidding strategies, and target the right keywords through analysis.What Is a Session?In digital analytics, a session measures a user’s interactions on a website within a given time frame. A session begins when the site loads and ends when the user closes the site or is inactive for 30 minutes. It encompasses pageviews, events, and other actions the user takes.To increase session duration, enhance site experience—improve design, readability, navigation, and content to keep users engaged and complete your desired conversions.What Is CPS (Cost Per Session)?CPS measures the cost of each session driven by your ads. Since sessions reflect active user engagement on your platform, Cost Per Session shows how much each session costs based on your ad spend.What Is ROAS?ROAS (Return on Ad Spend) measures your ad performance in terms of cost versus revenue. It represents the profit you earn for every unit of currency spent on ads. Since brands aim to get a strong return on investment, tracking ROAS provides powerful insights into campaign success.For instance, even if an ad brings in many high-quality users and revenue, it’s not a cost-effective spend if the cost exceeds the revenue. ROAS tracking helps you avoid such scenarios.What Is CTR (Click-Through Rate)?CTR measures the percentage of ad impressions that result in clicks. It indicates how relevant and compelling your ads are to your target audience. A high CTR is critical in PPC, as it affects Quality Score and indirectly the cost per click. Google Ads and other platforms reward high-quality ads with lower costs.If a competitor pays the same CPC but has a lower Quality Score, you may pay less to achieve a higher ad position. You can boost CTR by including search terms in headlines and descriptions, adding URL paths, and targeting the right keywords.In display campaigns, where creative visuals matter most, CTR offers insight into the effectiveness of your creatives.What Is a Lead?In marketing, a lead typically refers to a form submission generated by your campaigns. Each lead represents a potential customer. However, a high lead count doesn’t always mean campaign success—lead quality matters most. That’s why defining your buyer persona and targeting accordingly is crucial.To convert leads into sales, provide personalized content, maintain close collaboration with your sales team, and guide users along the funnel.What Is CPL (Cost Per Lead)?CPL measures how much each lead costs you. It shows whether you’re allocating your budget effectively. By comparing CPL across channels, you can reallocate budget from high-CPL channels to those generating leads more efficiently.CPL = Total Ad Spend / Number of Leads.What Is Search Impression Share?Search Impression Share is the ratio of impressions you received to the total impressions you were eligible to receive. Eligibility depends on factors like audience targeting, approved ads, bids, and Quality Score. For example, a 50% impression share means you appeared in half of the available auctions.If your impression share is low, you’re missing potential customers. Increasing impression share relies on high Quality Scores, sufficient bids, and adequate budgets.What Is Top Impression Share?Top Impression Share measures how often your ad appears above organic search results. If you notice your click-through rates dropping, it may indicate you’ve lost a top position.What Is Absolute Top Impression Share?Absolute Top Impression Share is the percentage of your total impressions that appear in the very top ad position. It’s calculated as impressions in the top position divided by the estimated eligible impressions for that position.What Is Bounce Rate?Bounce Rate is the percentage of users who leave your site after viewing only one page without taking any action. Tracking bounce rate helps you assess site experience. A high bounce rate can signal issues such as irrelevant content, slow loading times, or poor mobile optimization.To lower bounce rate, define your audience accurately, optimize page load speed, and use clear, compelling CTAs.Bounce Rate = (Single-Page Sessions / Total Sessions) × 100.What Is Frequency?Frequency measures how often a user sees your ad. Multiple impressions can help undecided users purchase, but if they see the ad repeatedly without converting, you may need to broaden your audience or adjust spend.Very high frequency can annoy users and harm your brand. Expand your targeting or reduce budget if frequency is too high. Ideal frequency varies: it’s lower for new-user campaigns and higher for remarketing. Determine your target frequency by reviewing past campaigns to find where CPA was minimized.

Oct 21, 2022 2874 reads

Market Basket Analysis

In today’s world, after the very well known pandemic we had through, the e-commerce sector rose like the sun. With this raise, websites of almost every brand from relatively small businesses to the largest businesses have gained a lot more popularity and their traffic has increased almost 50%. According to the International Trade Administration (2021), an average of 19% increase in e-commerce revenue is forecasted (26% in Food & Personal Care products) after the pandemic.These growth statistics and developments tell us one significant thing that the businesses should start to give more attention (and more budget) to their e-commerce/marketing departments and their operations. When e-commerce is mentioned, the first thing that comes to mind is obviously websites. Products are being tried to be exhibited in websites in such ways that help business owners to sell more products and gain more revenue from their most valuable resources which are customers. There are tens of ways to take the attention of customers and give them the intent of simply buying more. In this article, I will try to explain a method called “Basket Analysis”.What is Basket Analysis?Basket Analysis is a method that research and study on the baskets (carts) of customers in the website and analyse them to offer meaningful and customised product suggestions to the customers. Before getting through to the technical part of the analysis, there are some more things that we should better mention.Every customer is different as well as their purchase behaviour.Every product is different. However, some of them are used and bought together.In some situations, very unrelated products are sold together and human eyes sometimes cannot determine these ones.Exactly here, artificial intelligence and machine learning enter the stage.Let’s dive deeper into algorithms.Apriori AlgorithmThe Apriori algorithm has been in our minds since 1994 and it helps us find frequent item sets in a dataset for boolean association rules. Name of the algorithm is Apriori because it uses prior knowledge of frequent item set properties.In this algorithm, as mentioned above, the dataset must include products that are frequently bought. The data we need to apply this algorithm includes the following columns:Transaction ID (Basket ID)Product SKU (Product ID)Product CategoryQuantityAfter we obtain the necessary data, the magic starts.This algorithm can be written and applied by R Studio, Python etc.Since our data is breakdowned and thus has duplicate transaction ID’s, first we need to group the data by transaction ID and learn every distinct product that has been sold (obviously added to basket before the payment step) in that specific transaction.After this is done, we get dummy variables of all products and create new columns for each of them. For every unique transaction row, the quantity of the product is written to the cell of its own column and transaction. A sample processed data can be seen below:Transaction IDProduct AProduct BProduct CProduct D123abc7300456def2011Machine learning has its own rules, obviously. In order to analyse this data and have meaningful insights, we need to encode the cells into 1-0 to determine which product is added to the basket and bought in that specific transaction. The reason to do this is that Apriori Algorithms takes only 1 and 0 values to determine the association between products without any bias such as unrelated quantities. Consider that we are only interested in products that are being sold together.Finally, before we apply the model, we yield the below data:Transaction IDProduct AProduct BProduct CProduct D123abc1100456def1011We use the “frequent_patterns“ tool from “mlxtend” library and import “apriori” and “association_rules” packages to apply the model in an optimised and fast way.After the necessary parameters are adjusted in the model according to the specific dataset and specific purpose, we get the results as a table below:AntecedentsConsequentsAntecedentSupportConsequentSupportSupportConfidenceLiftLeverageConvictionProduct AProduct B0.40.60.50.832.780.0221.67Product AProduct C0.40.30.450.652.110.11.12Product BProduct D0.60.50.50.621.98-0.321.43Note: Values are randomly generated due to privacy issues.Results:OK. But What do they Mean?Here, the most important metrics that we should consider are “support” and“confidence” values. However, you can read the explanations below for a better understanding.Antecedent Support: The rate of the presence of antecedent products over all.Consequent Support: The rate of the presence of consequent products over all.Support: The rate of presence of antecedent product and consequent product being together in basket over all.Confidence: The confidence rate of products’ being together in the same basket.Lift: Confidence over expected confidence.Leverage: The statistical independency rate of a specific basket according to including products in it.Conviction: Gets higher when the consequent product is highly dependent on the antecedent product.After we yield the result table, we can start to analyse the results. How we do this analysis is according to some statistical methods. We should determine a threshold value for the “confidence” metric and split the rows into two parts: Meaningful or Not.When the confidence value is more than the threshold value, say 0.6, we can conclude that this relationship between products is meaningful and customers frequently buy these products together.Where to Use these Results?Where to use these results is another challenge.Businesses usually use this information for suggestion algorithms and shelf design.For instance, Product B is suggested to the customer who has just added Product A to his/her basket because the confidence level of these products is higher than our threshold value. Thus, the possibility of customer’s missing, forgetting or just not being interested in Product B is decreased and thus, we are being able to direct the customer to buy Product A.Secondly, shelf design (product listing pages in our case) can be conducted and applied to our website according to the results. For instance, Product A and Product B are located near each other to remind customers that they can buy them together (because they generally do it, don’t they?!).Thirdly, campaign scenarios can be set up for customers. For instance, Product B is presented with discounted prices for those customers who add Product A to their baskets or simply buy them before.Last but not least, the results of this analysis can help the business owners and marketers to design their offline (physical) stores’ shelves. Just like the product listing pages in websites, stores shelves can also be designed in such a way that customers can see related and frequently bought products together.In these ways, sale amount, order amount, revenue, traffic that website gains and key performance indicators like these may be increased. Besides, product & marketing costs can be allocated according to the results.

Oct 21, 2022 4436 reads

Product Scoring Algorithm

You are curious about performance of your products. And also, you are worried about how you can manage your product scope to increase revenue. Here are the answers for all of your questions, check out our blog and learn about our product scoring algorithm.Common problems in product managementThere are a number of questions that professionals might have in their mind while evaluating their companies’ performance. And some of these questions are more beneficial when thinking about important actions to increase efficiency of products that are sold on websites, apps and marketing channels.Here are some “general” questions that each team might be curious about the answers to. Which e-commerce metrics should we focus on while considering inventory management? Which products are more valuable? How can we increase our revenue by having the same product scope? What should be our key metrics for optimising marketing campaigns of our products? How should we decide our product listing in our website or app? Yes, you are right! AnalyticaHouse Data Science & Insights Team have another magical algorithm that would be helpful for all mysterious questions above and more, which is Product Scoring Algorithm. In this article, we will be talking about the structure of this algorithm and applications of it in the digital marketing sector.How is the algorithm built?There are a lot of variants for product groups in each website and app differentiating based on various dimensions such as colours, sizes, product types, season/offseason, target genders, target age groups and so on. We would be able to analyse every detail of products and product variants thanks to the Product Scoring Algorithm depending on the depth of the data we have.The minimum data requirement for this analysis are transactional data of products and current inventory information of them. We are focusing on these metrics while analysing transactional data; number of purchases, total quantity, revenue, average price and so on. Of course, this analysis would be more interesting when we have user/session-based data. We would be able to add other important metrics such as product detail views, add to cart, # of buyers, # of users who look at the product, revenue/product detail views, transaction/product detail views and so on. These metrics can be grouped into parts according to results they show;Revenue supporting metrics : number of purchases, total quantity, revenue, average price, revenue/product detail views, transaction/product detail viewsMetrics that show customer interest : product detail views, add to cart, # of buyers, # of users who look at the product, revenue/product detail viewsMetric definitionsnumber of purchases: Number of transactionsTotal quantity:  Number of products that are purchasedRevenue: Total amount of revenueAverage Price: Average price of productsrevenue/product detail views: Revenue per product detail views for each producttransaction/product detail views: Conversion rate (based on product detail views)add to cart: Total number of products that are added to cart# of buyers: Total number of customers who purchased related product # of users who look at the product: Total number of users who viewed related productApplication of the modelThe algorithm can be applied to each product level and can bring insights for bigger picture and tiny details.The example below shows a category/product level for an e-commerce company;Level 1: Shoes/BagsLevel 2: ShoesLevel 3: SneakersLevel 4: X Brand’s SneakersLevel 5: X Brand’s White SneakersLevel 6: X Brand’s White Sneakers with Size:38How is product score calculated?Product Scoring Algorithm is basically formed from two parts. The first part is based on calculating ratio of lower levels (eg. X Brand’s White Sneakers with Size:38) in totals of higher levels (eg. X Brand’s Sneakers) by using metrics that we discussed above, and we will explain this part by showing an example. The second part is dependent on current inventory information of products, and this part is very crucial when we take inventory management into account for short term and long term. Example above shows a product scoring calculation by using metrics add to cart and quantity sold in addition to instant inventory score ( if there is inventory it is 1 and if not it is 0). In this example distribution of level 6 in total of level 5 for add to cart and quantity sold metrics and inventory score are used as three multipliers of product score of level 6. After calculating the score of each row (each breakdown, for this example it is level 6), we calculate the score of the higher level ( in this example it is level 5 ). We take the sum of the score of all breakdowns for level 5 and as a result we have the total score of level 5. For example; for X brand’s White Sneakers the total score can be calculated as :0,0747 + 0,0245 + 0,0036 + 0,0028 + 0,0251 + 0,0000 = 0,131In this example X brand’s Black Sneakers has the highest score (0,193) and X brand’s Red Sneakers has the smallest score (0,071).How can we use the results?This analysis can be applied to all levels from bottom to top. And of course, this analysis can be enlarged with additional metrics that we talked about before and results can be calculated in more detail. In addition to that, we can change the importance of each metric in the calculation step of the product score by using Variable Importance Analysis which is another outstanding algorithm that our team improved. Last but not least, results of the analysis can be used for updating product feed automatically for marketing campaigns. 

Oct 17, 2022 664 reads

6 Reasons to Buy Media in November - AnalyticaHouse

Everyone knows that the November sales events, which even consumers who have never shopped online know well, hold special importance for both e-commerce brands and consumers. The Black Friday phenomenon, which began years ago in the U.S. and quickly took hold in our country as well, has extended beyond its original date (the last Friday of November). Seeing the potential of this period, brands with "bright" ideas have spread it before and after that date with various special days (11.11, Cyber Monday, etc.), turning the entire month of November into a month of deals in e-commerce. In just a couple of years, the term “Black Friday” has evolved into “Black November.” As you know, digital media channels outside the main performance marketing channels (Google, Meta, etc.)—such as Onedio, Ekşi Sözlük, Kızlar Soruyor, Admatic, Medyanet, Sahibinden.com, etc.—do not operate on a retargeting logic, and are therefore usually positioned in the user funnel for new user acquisition. Considering that new users on e-commerce sites typically make a purchase on their 7th–8th visit, expecting first-time visitors from these channels to complete a purchase in their first session (last click non-direct transaction) is not very realistic. In this article, we wanted to address the missed opportunities that arise when, looking at digital marketing activities as a whole, we focus too much on concrete metrics like ROAS and their primary inputs (search, retargeting, affiliate campaigns, etc.), while sidelining less directly measurable inputs like brand visibility and awareness. 1. Volume Problems in Performance Advertising In November, e-commerce brands of every scale are incentivized to run eye-catching promotions. Especially marketplace-based e-commerce brands (Trendyol, Hepsiburada, etc.) increase both their brand and performance marketing budgets to reach more people or drive existing users to shop. During this period, advertising slots on digital media channels see high demand due to the huge budgets of e-commerce giants. On the other hand, these channels prioritize selling inventory directly to brands that can purchase it outright; any unsold inventory is made available via Real Time Bidding (RTB) to Google and programmatic buying platforms. Therefore, brands relying solely on Google or programmatic display buys in peak season get stuck with lower-impact inventory. And because demand is high there too, they end up paying higher CPM or CPC to remain competitive in RTB auctions. 2. The Need for Extra Reach and Gain from Share of Voice As in traditional retail, clearance events in e-commerce make sense only if you bring in new customers. The discount model works by sacrificing margin to drive volume and maximize total profit. Therefore, performance marketers’ top priority must be to reach new users outside their core base during promotional periods. Thus, opening reach- and new-user–oriented display campaigns on Google, Meta, and other RTB platforms is necessary—but in November, due to the reasons outlined, their impact alone will fall short. 3. Visibility Loss and Ad Blindness in Performance Ads As noted in point 1, the most visible slots get sold direct, leaving lower-impact RTB inventory. Further, performance campaigns optimize for clicks or conversions, sidelining the creative impact of the banner itself—and thus any awareness benefits. 4. Rising Unit Costs and the Need for Alternative Channels Some non-performance platforms charge on a CPC basis. While more cost-effective than performance channels in normal times, in November they lose that edge due to high RTB bids. Thus, non-performance platforms become part of a holistic performance plan—Engageya, Medyanet, Ciner, etc., all offer CPC models. 5. Turning Increased Demand and Purchase Motivation into Opportunity Consumers tend to postpone non-essential purchases until big sale events—and casual browsers are more inclined to buy under the influence of heavy promotion. This “buying mood” benefits both big and small brands alike. 6. Indirect Performance Gains from Higher Brand & Campaign Awareness Performance ads target users most likely to convert, but brand campaigns raise overall awareness, strengthening later organic and paid performance. Regular media investment boosts search volume on Google, which yields the most valuable and cost-effective traffic for e-commerce. Google’s RTB rewards ads with higher click-through rates (CTR) by lowering their CPC. By positioning media buys as brand investments in November, brands can boost CTR, reduce CPC, and achieve lasting efficiency gains. In short, brand marketing and media buying become even more essential and effective in November. We recommend that all digital brands treat marketing holistically—balancing performance metrics with the brand awareness and positioning gains achieved in consumers’ minds.

Sep 5, 2022 1 reads

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