How can you optimize your search ad bidding strategy with historical data?

Dig deeper than clicks. Analyze impressions, conversion rates, CPA, and ROI. Identify hidden patterns by device, time, location, and demographics. Use multi-touch attribution models. Anticipate trends with historical data. Automate bidding, but monitor performance. A/B test relentlessly with different bids, ad copy, and targeting options.

AMAZON SICHT Bei der reinen Attribution von Sales auf die Klicks sollte man unbedingt Daten auf 1 7 14 oder 30 Tage mitnehmen. Das Kaufverhalten bei Staubsaugern ist ggf au?erhalb der angezeigten Attribution und die Werbung damit schlechter als sie wirklich ist. ?hnlich ist es mit den Betrachtungszeitraum. Hier sollte man ggf statt kleiner Zeitr?ume und vielen Anpassungen auf l?ngere Zeitr?ume zurückgreifen und so Entscheidungen dank mehr Daten treffen. Diese 2 Zeitebenen helfen direkt bessere Einstellungen zu treffen

The history of the campaign gives a clear picture of the performance. 1. Cpc optimisation for low conversion ads, keywords or bid strategy. 3. Audience testing 4. Product page experience, avg session time.

Diving deep into your past campaign data helps to understand what worked, and what didn’t. Besides this, looking at metrics such as click-through rates (CTR), conversion rates, cost per click (CPC), and return on ad spend (ROAS) is beneficial to optimize you ad bidding. I hope this helps.

High Volume Generic Searches: ~75% searches are generic keywords (India). Maintain a high impression share (organic & ads) for these keywords. Stagnant impression share on high volume keywords hinders growth. Branded Search Volumes: This relies heavily on external activities. If it's not growing, the dependence on Ads for visibility persists, hindering profitable growth. Also increased competition in branded searches makes retaining market share challenging. Conversion Rates (CR): Analyze conversion trends for high volume keywords. If CR isn't improving despite increasing impression share, revisit product pricing and proposition. Stagnant CR may indicate reaching market saturation, requiring new propositions to expand the target audience.

Your goals are going to vary by product, NTB vs repeat targeting, competitiveness, and other factors. However setting goals at a product level is key. It allows you to operate a variety of advertising strategies rather than trying to fit one ad strategy into one overarching goal. Other pro tip- certain benchmarks can be misleading cnvrs rates for example on low price point items are going to be much higher than expensive items. Roas can look higher against more expensive items even with lower cnvrs because of aov. Factor these things into your marketing plans

Setting clear, quantifiable goals and KPIs is critical. Whether it's boosting sales, increasing brand visibility, or driving website traffic, each goal demands a tailored approach. Drawing from past roles, I've learned the importance of aligning these goals with broader business objectives. Setting realistic KPIs, such as CPA or ROAS for sales-oriented campaigns, ensures that every dollar spent is an investment towards achieving these targets. This process demands a blend of ambition and pragmatism, rooted in historical performance and industry benchmarks.

In my experience Set clear, measurable goals and KPIs tailored to objectives like sales growth, brand visibility, or website traffic. Align these targets with broader business goals, drawing on past experiences for guidance. Establish achievable KPIs, such as CPA or ROAS for sales, ensuring every dollar spent drives toward these objectives. This approach blends ambition with pragmatism, informed by historical performance and industry benchmarks.

Determine which keywords have historically led to the most conversions. Consider increasing your bids on these high-performing keywords. f your data shows that certain times of day or days of the week perform better, adjust your bids accordingly to maximise visibility during these times. If your campaign performs better in certain locations, consider increasing your bids for those specific areas.

Utilize your historical data to set achievable targets for each KPI. Analyze past performance to understand what has been possible and what improvements you might realistically aim for. Also, consider industry benchmarks to see how your targets compare to standard performance metrics in your sector.

Amazon Sicht Hierbei muss man besonders aufpassen was man einstellt und wo Es gibt Einstellungen auf Ebene des Account, des Portfolio, der Kampagne und auf Ebene des Targets. Einstellungen wirken dabei auf den gesamten untergeordneten Bereich. So sind von Kampagneneinstellungen wie Modifier alle Targets aller Anzeigengruppen der Kampagnen betroffen

Select a bidding strategy that best aligns with your goals and KPIs, such as manual bidding, automated bidding, or a combination of both. Consider using automated bidding strategies offered by the advertising platform, such as target CPA, target ROAS, maximize clicks, or enhanced cost-per-click (ECPC). Determine the bidding method based on your campaign objectives, budget constraints, and level of control desired over bidding decisions.

The choice of bidding strategy is crucial for the success of your campaigns. From my experience, my advice is: - Favour manual bidding for new campaigns to maintain control and flexibility. - Switch to automated bidding as soon as you have collected enough data for effective optimisation. - Consider your budget limits and the competition to set realistic bidding targets. - Experiment with different methods on a small scale before implementing them fully. This approach makes it possible to continuously improve the efficiency of campaigns and make targeted adjustments.

The choice of bidding strategy and method for your campaigns involves considering manual and automated options. Manual bidding allows for control and customization of bids, but can be time-consuming. Automated bidding uses algorithms to optimize bids based on goals and KPIs, saving time and resources. Factors to consider include budget, campaign complexity, and competition. A hybrid approach combining manual and automated bidding may also be effective. It's important to experiment and test different strategies to find the best fit for your needs.

Continually test and refine your bidding strategy. The more data you accumulate, the more accurately you can optimize your bids. Many platforms offer automated bidding strategies that use machine learning to optimise bids based on historical data. Analyse performance across different devices. If mobile users convert more often, it might be worth increasing bids for mobile. If your campaign performs better in certain locations, consider increasing your bids for those specific areas. The goal is to maximise ROI. Higher bids can lead to more visibility and more clicks, but they also cost more. It’s important to find a balance that maximises conversions while keeping costs under control.

The dynamic nature of search advertising demands constant testing and optimization. My approach has always been experimental, leveraging tools like Google Ads Experiments to run A/B tests on different bidding strategies. This hands-on experimentation, informed by historical data, allows for fine-tuning bids to maximize efficiency and effectiveness. Seasonal trends and market shifts are also crucial considerations, requiring adaptable bidding strategies that can capitalize on these changes.

Conduct A/B testing to compare different bidding strategies, ad creatives, and targeting parameters. Monitor the performance of your campaigns in real-time and adjust bids accordingly based on emerging trends and opportunities. Implement bid adjustments for factors such as device type, location, time of day, and audience demographics to optimize performance. Continuously refine your bidding strategy based on the analysis of ongoing campaign data and performance metrics.

In order to improve the effectiveness of your bids for future campaigns, it is crucial to continuously test and optimize them. Monitoring campaign performance and comparing it to your goals and key performance indicators (KPIs) is essential. Experimenting with different bid amounts and analyzing their impact on results is recommended. Google Ads Experiment and Microsoft Advertising Experiment tools can be utilized for running split tests and measuring the effects of bid changes. Historical data should also be taken into account to identify any seasonal or cyclical fluctuations in demand and adjust bids accordingly.

Analyze historical data to identify patterns of seasonal demand or market trends that could influence your bid strategy. Adjust your bids in anticipation of these fluctuations to ensure your ads remain competitive during peak times and cost-efficient during slower periods.

The best ways will be: - Establish clear goals (if it is maximizing conversion or revenue goals) - Segment campaigns: by taxonomy, demography, audience, behavior etc - A/B testing: test bid strategy, run campaigns with different bidding strategies (e.g. manual bidding vs. automated bidding). - Optimize bids on historical data: identify high-performing keywords, products, or audiences. - Analyze bid adjustments: device type (mobile, desktop, or tablet), location (geographical locations), time period (find times when target audience is most active). - Adjust bids automations (impression shares, bidding up and down etc) - Monitor KPIs

Beyond bidding, consider other elements that impact your ad performance, such as ad copy, landing page experience, and audience targeting. Also, keep an eye on industry trends and algorithm changes by search platforms. I hope this helps.

Other things to consider - Competitive intelligence: Analyse competitor ad spend, keywords, and landing pages to identify gaps in their strategy and exploit them with targeted bids. Negative keywords: Continuously update your negative keyword list to refine targeting and prevent irrelevant searches from draining your budget. Stay updated: Search advertising platforms and industry trends evolve rapidly. Stay informed and adapt your bidding strategies accordingly.

Take seasonality and market changes into account to adapt your bidding strategy. Use machine learning and automated tools to recognise patterns in large amounts of data and dynamically adjust your bids. Integrate competitive analyses to understand your position compared to the competition and react accordingly. Rely on continuous training and stay informed about the latest trends and technologies in search engine marketing.

The history will help us inform how the future could look like, but can never 100% predict the future. So don’t just built your whole process around historic data

Test against your historical data with small budgets and constantly. If you only optimize in regards to your history, you will never discover new revenue streams and you will not be prepared for a sudden shift in the markets; but we have seen too many black swans in the past years. So prepare, by building hypothesis against your historical data and and experiment.