PPC, LEAD MANAGEMENT, CRM

We focus on campaign targets such as brand awareness, traffic or conversions. With good campaign setup we can increase the revenue with target ROAS bid strategy or website traffic with optimazation to clicks. We setup the campaigns on Google, Meta platforms or LinkedIn depending on business demands and campaign types.

Holistic Digital Campaign Planning

We start with discussions about campaign goals, key values, unique selling proposition, messages, goals and KPIs are important for your digital marketing engine.
Based on received inputs we create a proposed campaign plan.

Digital Marketing Components

We orchestrate various digital funnel improvement activities (SEO, PPC, Site conversion rate optimization, onboarding, X-Sell, CRM, retention, recommendation) with holistic marketing mindset. These have cross impact on each other therefore improvement tasks are executed parallely the get the highest results in the shortest possible timeframe. Good SEO brings down Cost per click, digital ads helps search engines learn faster getting better and better organic positions. Site conversion rate improvement also helps ad platforms machine models learning faster to optimize audiences for conversion and ROAS. Site conversion rate optimization CRO and Product Recommendation Engine improves average order value AOV, ebit per user, ROAS.    

Ads (PPC) Campaign Execution

We discuss the campaign execution plan with the customer and after approval set up the campaigns.

We make sure all prerequisites are set. SEO, page conversion optimization, ads accounts, performance measurement, marketing automation activities are all in sync to support business long-term goals and short-term objectives.

While in most of cases the creatives are provided by the customer, we can work together with 3rd party creative agency.

Machine Learning in Google Ads Campaigns

Google Ads, formerly known as Google AdWords, utilizes machine learning to improve the performance of advertising campaigns for businesses. Some of the ways machine learning is used in Google Ads include:

Smart Bidding: This feature uses machine learning algorithms to optimize bids for individual auctions, taking into account factors such as device, location, and time of day to help businesses get the most out of their advertising budgets.

Predictive Bid Simulator: This tool uses machine learning to predict the potential impact of bid changes on an ad campaign’s performance, allowing businesses to make informed decisions about their bids.

Audience Segmentation: Google Ads uses machine learning to segment audiences based on their browsing history and interests, allowing businesses to target specific groups of users more effectively.

Ad Optimization: Machine learning is used to optimize ad creative and ad copy, helping businesses to create more effective ads that are more likely to be clicked on.

Forecasting: Google Ads uses machine learning to forecast the performance of ad campaigns and make predictions about how changes in targeting or ad creative will impact performance.

Overall, machine learning plays a key role in helping businesses to improve the performance of their Google Ads campaigns, by making it easier to target the right audiences and optimize ad creative for maximum impact.

Machine Learning in Meta/Facebook Ads Campaigns

Machine learning can be applied in various ways to optimize pay-per-click (PPC) advertising on platforms like Facebook and Meta. Some examples include:

Audience targeting: Machine learning algorithms can analyze data on user behavior and demographics to identify and target specific segments of the audience that are more likely to convert.

Bid optimization: Machine learning can be used to predict the likelihood of conversion for different keywords and adjust bids accordingly, maximizing return on investment.

Ad personalization: Machine learning can be used to personalize ads to specific users based on their browsing history, demographic information, and other data points.

Campaign optimization: Machine learning can analyze data on campaign performance and adjust various parameters such as ad creative, targeting, and budget to improve campaign results.

Predictive analytics: Machine learning can be used to analyze historical data to predict future performance of PPC campaigns, making it easier to identify patterns and make informed decisions.

Overall, machine learning can help PPC advertisers to more effectively target and engage with their audiences, increase conversions, and improve campaign ROI.

CRM Campaign Execution

Best if we can manage campaigns holistically across customer lifecycle. Lead management nurturing is important part of customer journey boosts conversion rates significantly. We make sure that customer journey is covered with Lead management and CRM campaigns beside managing PPC campaigns.

We have hands-on experience with largest CRM platforms such as Salesforce, HubSpot, Sailthru.

Machine Learning Digital Funnel 

An AI digital funnel is a machine learning based technique that helps you automatically target and engage potential customers through personalized content. By automatically analyzing customer behavior, the digital funnel can identify the best time to reach each customer with relevant content. Additionally, the digital funnel can also help you segment your customers based on their behavior, so you can send them more targeted content in the future.

Digital Funnel performance optimization with AI 

Digital marketing teams can use machine learning to automatically identify the best acquisition channels and strategies for their business. By analyzing past customer behavior, machine learning can predict which channels are most likely to result in a conversion and optimize the acquisition funnel accordingly. This allows businesses to focus their acquisition efforts on the channels that are most likely to result in customers, saving time and money.

Deep learning models for customer management

1. Sentiment analysis: A deep learning model can be used to analyze customer sentiment from reviews, social media, and survey responses. This can be used to identify areas of improvement for the company, or to target marketing and customer service efforts.

2. Customer segmentation: A deep learning model can be used to segment customers based on their purchase history, demographics, and other data. This can be used to customize marketing and sales efforts, or to improve customer service.

3. Next-best-action prediction: A deep learning model can be used to predict what the next best action is for a customer, based on their past behavior and current context. This can be used to automate customer interactions, or to improve customer service.

4. Churn prediction: A deep learning model can be used to predict which customers are likely to churn, based on their past behavior. This can be used to prevent customer attrition, or to target retention efforts.

AI models for hyper personalization

Deep learning models can be used for hyper personalization in a number of ways. For example, they can be used to create personalized recommendations, to segment customers, or to target ads.

1. Personalized recommendations: Deep learning models can be used to create personalized recommendations for customers. For example, a model can be trained on a customer’s past purchase history to make recommendations for what they should buy in the future.

2. Segmentation: Deep learning models can be used to segment customers into different groups. This can be useful for targeted marketing campaigns. For example, a model could be trained to segment customers based on their demographics, interests, or behavior.

3. Targeted ads: Deep learning models can be used to target ads to specific customers. For example, a model could be trained to target ads based on a customer’s demographics, interests, or behavior.