Month: July 2020

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Blog

Issues that Can Occur When Email Order Czech Brides Is usually Using

Mail Purchase Czech brides is known as a way to get married within a different nation. There are many reasons why people from the old West decide to marry overseas. A large number of people accomplish this for trip or for the challenge of marrying somebody from a unique https://ukraine-brides.org/czech-brides/ culture. However , additionally, there are cases in which the reason is because of the groom or the new bride has some kind of medical condition that needs travel and in addition they wish to wed a person who might take care of all of them while they are away. Either way, it’s not at all times easy for somebody who doesn’t speak English going abroad to get married, especially if it’s the first-time.

Exactly what do you expect if you choose to marry via deliver order? The bride is generally from a unique region than the bridegroom is right from a completely varied country. Deliver order brides to be might arrive from Germany or from The country of spain or any volume of other Europe. The mail purchase bride can fill out a credit application form in order that the bride businesses in European countries can find her a meet.

After you have found your match, the bride organization will start organizing the required docs for you to move before the judge. You might need to prove your identity, which is one of the most significant requirements. You will also need to tell them how long curious about been living within your new nation and how much money you have in the financial institution. Many all mail order brides to be will need to obtain a lawyer to enable them to go through the paperwork and make sure every thing is lawfully accurate.

While many people are thrilled on the idea of marriage via -mail order, there are lots of things which might be problematic. One of the greatest complaints about email order partnerships is that you’re not able to discover anybody who’s betrothed to you. If they avoid send you frequent updates on the whereabouts, then you might never find out whether they’re having problems or perhaps not. A large number of people have lamented regarding not being able to travel to the home belonging to the bride or maybe the groom to ensure they not necessarily in any hassle.

An alternative big problem is the fact many people end up getting in to very serious economic and legal challenges because they didn’t know the conditions set up by the matrimony. There are plenty of situations when people possess split up not realising this. Because of this, the bride or perhaps groom may well end up leaving the country while not informing the partner. This really is dangerous for some reasons. In case the mail-order new bride doesn’t advise their partner, then they may well end up visiting another country on the plane, which in turn would lead to them downloading copyrighted movies and beginning a new lifestyle.

Submit order brides can be a good way to get married. They can end up being a real trouble for the folks involved. It is recommended to make sure you find out what’s going on and that everyone is knowledgeable. Submit order brides are becoming widely used and with this increase, there are many issues that can come up. The best thing you can try is discover a reliable marriage agency and maintain an vision on things so you can not end up in a bad situation.

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Blog

Is normally Mail Purchase Spouses Actual?

If you have a great old-fashioned idea that matrimony is a contract for ease, then a deliver order significant other might be suitable for barranquilla colombia women you. A snail mail order partner might sound a https://brightbrides.org/blog/barranquilla-dating-guide little strange, however it is a very serious way for individuals to meet others who are older and want to get married. Anybody looking for some other person will fill in a form declaring that they are interested and will need to find out more information.

The mail order spouse will then be sent a questionnaire. It will usually inquire about their significant other status and sometimes what religion they are. After you have all this information you’ll be given a listing of possible fits. Some -mail order sites will allow you to perspective pictures as well as videos of your mail purchase spouse prior to you choose them to match.

If you meet with a mail purchase spouse then you definitely will need to register a merchant account with that mail order internet site. Some sites require you have a credit card to enable them to charge pertaining to shipping and also other charges. One does have the directly to decline any mail order provides and end any payments that you are making.

Reaching a email order significant other is less simple united would think. When you are dealing with people who are certainly not honest and simply want to take advantage of others, points can get complicated. Be careful so, who you give your own personal info too, because you could end up with a serious scam. Even though postal mail order going out with has been around for quite some time, it is continue to a very risky thing to provide out your personal information online. There are serious dangers involved if you give out your own card numbers online, especially if the mail order spouse wouldn’t make you a deal.

Generally the mail purchase spouse websites are legitimate but there are some scams out there. Make certain you check out the web page very carefully prior to you give any cash or signal anything. The scammers definitely will promise you undying love and then try to get more money away of you.

In due course, the only one who really advantages from mail order romance is the scammer. While this technique of getting together with a postal mail order loved one might sound interesting, it’s best to simply just stay away from all of them. If you feel that you’ll be truly considering having a significant relationship, then go ahead and test it. But do not let it obtain too far ahead of you. You need to be mindful and satisfy find someone who is truly in to the mail purchase dating matter before opting for it.

Categories
Video Analytics

A pilot study to count the number of vehicles passing by an OOH billboard

A digital Out of Home (DOOH) agency commissioned a pilot study to assess the benefits of using a computer vision based vehicle counts against the sampling based measurement techniques that they currently use. They wanted to look at the feasibility of the solution in terms of long term cost of equipment maintenance, light and weather related challenges and the cost of processing data and maintaining the data infrastructure.

Tracking and counting using Computer Vision

The DOOH client had commissioned the pilot to assess the benefits of using Deep CNN based vehicle count to measure the ad views compared to sampling based approach that they use.

The solution accuracy was impacted by light and weather conditions

Sampling based estimations had an acceptable 2.7% error

The operational and data processing costs outweigh the benefits

The study established that the deep learning based solution did not offer any significant benefits over the current practice of ad views estimates.

Challenges in training and variety of vehicles.

The digital out of home (DOOH) had read about the Deep CNN based methodologies to count the number of vehicles, and hence the ad views for their ads. They wanted to assess the feasibility of deploying such a solution on their OOH properties. They commissioned a pilot for 3 properties at strategically chosen locations to analyse the effectiveness of the solution and long term feasibility.

One of the major challenges was to collect enough data for training the Deep CNN algorithm. Due to different sizes of vehicles, the performance of the solution remains a challenge and affects the accuracy of vehicle counts. Another challenge was the overhead cost associated with the physical infrastructure to be maintained at every OOH property. The bandwidth and data processing requirement was another challenge.

A solution not ready for commercial applications

Deep CNN based vehicle count is a solution that looks good on paper and a demonstration of what artificial intelligence and machine learning solutions can do. However, the cons far exceed the pros for commercial applications – at least as of today.

a. The operational cost associated with acquiring equipment and the maintenance associated with it is prohibitive

b. The huge bandwidth needed to process high quality, streaming video data and generate counts

c. The accuracy of the algorithm – affected by lighting, weather and other adverse weather events

All the above, with no significant benefits over the current sampling based estimation methods that they currently use.

Categories
Text Analytics

Keyword trend prediction on social, news, eCommerce & search

The US based media agency aimed to bring the concept of high frequency share trading to the keyword bidding process. They wanted to identify the trending keywords based on user posts on social platforms, breaking news from news websites, products with high search volumes based on popular e-commerce platforms and web search trends on search engines. They would then identify and bid for keywords that are most likely to generate higher eyeballs for their client’s digital ads.

Real-time holds the key

The media agency wanted a (near) real-time platform to process the enormous amount of data and generate keyword association and topic modeling to present the trend results.

Predicted views in the next three hours using historical volumes

Able to predict eight out of twenty trending topics on twitter on an average

Developed a web based platform to combine prediction engine and reporting

The solution generated predictions with high degree of confidence between the predicted trends at T+1, T+2 and T+3 and historical chatter volume till time T.

High volume, high velocity challenge

The media client had conceptualised an innovative solution for serving it’s clients more effectively. The idea was to ride on the wave of popular keywords and place relevant ads so that the number of views are maximized. The scope of the data sources – social platforms, news websites, e-commerce platforms and web search was holistic, and covered almost everything to capture trends. The solution was conceived as a web based platform for ease of access.

There were multiple challenges in turning the idea to reality. The first challenge was the huge amount of data that was required to be processed in real-time. Another challenge was to process this data with text analytics driven algorithms that could process such high volume of data and generate the keywords, perform topic modeling and group similar keywords together – in (near) real-time.

Springboard for greater business outcomes

We designed a solution that could process more than 6GB of data per minute and extract the trend and topic insights with a lag of less than 5 minutes.

The solution was integrated on a platform, and the solution:

a. Predicted twitter trends at State level, capturing eight out of twenty keywords correctly, on an average

b. Twitter trends were captured at least 90 minutes before appearing in ‘Twitter Trends’

c. An accuracy of ~60% for ten defined categories of keywords

The media agency is now working on integrating the volume and bid price for digital ads.

Categories
Customer Analytics

Churn prediction of prepaid subscribers for a telco

Customer churn is one of the major problems of the telecom industry. Acquiring new customers is several times more expensive than retaining an existing customer. Hence, every telecom company has an active always on churn prevention and subscriber retention program. For this telco client, we used random forest based churn prediction model. The study is focused on identifying the triggers of churn – and address them through an effective retention strategy

A persistent problem for the telecom client

The demographic, transactional and behavioural data is used to visualize the entire subscriber data and identify the causes of churn

A prediction accuracy of 84% on the hold-out sample

Usage attributes (e.g. number of calls per month) are most important factors

78% churn captured in first three deciles reducing cost of retention campaigns

The telco was able to predict when a subscriber is most likely to churn and contact them with the right offer and improve life-time and average revenue (ARPU).

Churn is acquisition wasted and revenue lost

Customer retention is the key to growth. Given the high differential of the cost associated with acquiring a new customer compared to retaining an existing customer, the telco client is focused on churn prediction and building an effective retention strategy. And the first step to achieving that is through an effective churn prediction model to identify the subscribers early enough. Retention also improves the life time value and improves ARPU.

The challenge was to combine the vast amount of data on subscriber transactions and merge it with the demographic and behavioural data. Once the master data is created, the challenge is to ensure that the data quality is good enough to build a prediction model. Lastly, the right definition of churn has to be established – and the difference between a win-back and lost customer needs to be established.

A bird in hand is worth two in the bush

Random forest based churn prediction model enables the client test machine learning approach to predicting churn and identify the pros and cons over a statistical modeling approach.

One of the major benefits is a higher accuracy level achieved with random forest based prediction. A useful churn prediction model enables the telco to:

a. Identify subscribers who are most likely to churn and retain them through effective campaigns

b. Cover larger percentage of ‘at risk’ subscriber with lesser number of calls – allowing them to improve campaign effectiveness and lesser number of calls

The telco has identified key factors driving churn – and using the right promotions to cross-sell, upsell in specific geographies and for specific demographics.

Categories
Social Media Analytics

Deep insights from Vietnamese conversations on social media

The Vietnamese privately held bank was planning to engage with it’s fans on social media. They did not have a clue of the best social media channels to begin their social efforts. Besides, there are local forums that are more popular than Facebook and Twitter, given the cultural fit and the presence of more active and visible Vietnamese user base. They wanted to look at their competitors and identify the forums most suitable for a focused social media marketing efforts.

Social media doesn’t just drive opinion

The banking client used the analysis to generate insights – the most important being – social platforms are much more than opinion driving platforms.

Identified six social platforms – including four local and more popular ones

Reduction of 40% in turn around time (TAT) on user complaints

Competitor analysis and benchmarking at product and service level

The bank has a better understanding of the strength (and weaknesses) of each platform, and has used our analysis to build an automated reporting system for social media.

Non-English conversations. Local slangs.

The Vietnamese client is one of the Top 3 privately held banks in Vietnam. They are growing at a healthy pace and desire to engage with fans on popular social media platforms to boost brand visibility. They also wanted to look at competitors and establish themselves on platforms where competitors were already active and had a fan base.

There was one problem though – to build a manageable, scalable social media marketing function, they needed to utilize technology driven solutions. And all major solutions did not provide support for Vietnamese language – the primary language of communication on social platforms in Vietnam. Another significant problem was the use of slangs, spellings and language that was not standardized and could not be processed through an off-the-shelf solution. A third, but minor problem was the popularity of local social platforms.

One more window to look at the world…

The Vietnamese banking client was able to leverage our strengths in designing the Vietnamese natural language processing (NLP) based text analysis solution from scratch.

A major challenge for us was the identification on non-standardized language – slangs, local context and misspellings. We navigated these through local language experts. As a solution, we developed an automated monthly reporting system that allows the client to:

a. Identify posts and sentiments associated with the posts across global and local social platforms

b. Monitor the activities of the competitor

c. Extract product and services attributions in the posts and monitor sentiment on those

Categories
IOT Analytics

Estimate the economic changes using night-time illumination data

The PE firm makes investment decisions based on growth prospects of an economy. For this, they use economic performance indicators published by the countries. However, these reports are subject to manipulation e.g. China and data quality issues in developing countries where agencies do not have a lot of pressure on collecting accurate data. They wanted to look at a measurement methodology that is available faster than official releases and cannot be manipulated easily.

Identify activity clusters

Data gaps in developing countries are constraining and impacts the accuracy of the economic performance forecasts.

Economic activity estimates predicted within a week’s time

Accounted for the impact of cloud cover and pollution levels in estimates

Accounted for moon phase in illumination observations

The PE firm used the estimates from the model as an important metric to make investment decisions and have a more thorough analyses of investment decisions.

Data and measurement gaps

The economic activity data in developing economies suffer from two challenges – the first being availability of reliable data. This has more to do with enough investments in improving the data collection process and training the staff.

The second problem lies with the pace at which the data can be collected. Developing economies, with generally higher density of population (esp. China, India, Indonesia) make it difficult to collect data at a rapid pace. The staff take their own time collating data from far flung locations and coming up with the estimates, which need to be vetted by the respective government agencies. We proposed an inexpensive, open data source – NASA nigh-time illumination data that is available from their website on request and test if it can be used to estimate economic activity on ground.

A proxy for changes in the economy

In order to develop an effective economic activity estimation solution, the first thing we need is the ability to handle the large volume of data being generated by the satellites on a daily basis.

Once the above has been addressed, the external impact on the illumination readings should be taken care of through data cleaning process to:

a. eliminate the impact of cloud cover

b. eliminate the impact of cloud cover

c. eliminate the impact of pollution and smog

Time series decomposition of the albedo data yielded seasonality – for cloud cover, cyclicity – for moon phase, external air quality data for pollution effects. The final component is the proxy for economic activity.

Categories
Image Analytics

Automated meter reading (AMR) to drive efficiency

The utility service provider was looking at multiple solutions to drive efficiencies in their operational processes. They turned towards image analytics to assess if an automated meter reading solution would help them reduce the human resource requirement, increase accuracy and efficiency and solve their problem of unavailable customers (locked houses) and help them generate bills on time – improving the efficiency of the billing process and push customer satisfaction.

Machine learning based automation improves efficiency

The utility provider was looking at image analytics based automated meter reading (AMR) solution that could solve a lot of problems

An accuracy of 84% on untrained sample of meter images

Improved reading coverage of 4% in the pilot area

Improved payment compliance of 2.7% in the pilot area

One of the key challenges that emerged out of the pilot was no reading in a significant number of cases due to poor lighting in the meter location and unreachable meter locations.

Inefficient, human resource driven process.

As a regular practice, the analog meters are used to collect data for the energy consumed. The meters could have a number dial or more recently, digital displays are used. The utility provider’s employee visits each household with and notes down the reading at the end of every billing cycle. This process suffers from wastage of human labour, human error, manual process of reading the paper based notes every month to generate invoices.

This manual process is inefficient, time taking and cumbersome. Another big issue with the process is that it is not possible to take readings if no one is at home. This leads to delays in generating invoices, and in some cases clubbing bills for multiple months. The aim of the pilot was to showcase the current capabilities of machine learning based automated meter reading solution.

Latent benefits of Automated Meter Reading

Image analytics based automated meter reading solution allows customers to click the image of the meter through a web application. This process provides following benefits to the utility provider:

a. An automated solution generating efficiencies in the number of meters being read in each billing cycle

b. Enables timely and efficient generation of invoices, leading to a higher payment compliance

c. Freeing up human resources for other tasks and reducing human dependencies

The pilot though highlighted one of the key limitations of a machine learning based automated meter reading process. The best accuracy achieved was 84%, which was significantly lower than the manual process, which, despite all inefficiencies had an avg. error rate of ~2%.

Categories
Digital Analytics

Algorithmic attribution of digital touchpoints in conversion

The telco client was running a digital customer acquisition campaign. They were advertising heavily across multiple platforms – social media, popular news websites and through agency adtech platform. They wanted to study the impact their ads were having across different digital channels, and how each touchpoint was contributing towards final conversion of the customer. They were also interested in the RoI from each channel and the optimal touchpoint frequency.

Identifying where credit is due

The client wanted to optimize their digital marketing budget using data-driven insights and using them to generate maximum return on investments (RoI)

More accurate distribution of weightage across all touchpoints

Identified cost per acquisition (CPA) and recommendations to reduce CPA

Optimal number of touchpoints for best conversion propensity

The study helped the telco identify leakage in digital campaigns, and optimize their future campaign strategy to minimize cost per acquisition (CPA) and optimize campaign spend.

Too much data. Scarcity of expertise.

The telco was using the rule based attribution models – First-Touch, Last- Touch and Time-Decay based models. All these models were giving more than 80% weightage to Google touchpoints. The client was aware that other touchpoints were contributing more. They also wanted a visibility on the return on investments and the optimal number of touchpoints to maximize conversions.
The biggest roadblock to implementing a more data-driven approach to attribution was the massive volume of data being generated. They lacked the expertise to unlock the insights in conversion data. The other challenge was dealing with a highly unbalanced dataset with less than 0.2% conversion rate. Our team provided the capability to combine our data handling and modeling expertise to unlock the strategic insights.

Beyond Last-touch, First-touch & other rule based attributions

Algorithmic attribution helped our client unlock the key marketing insights that were hidden in their data.

With algorithmic attribution (or multi touch attribution or MTA), the client was able to unlock key insights, including:

a. A more accurate estimation of credits across all touchpoints vis-a-vis rule based attribution models

b. Cost per acquisition (CPA) estimates and better understanding of the touchpoints that work best

c. Optimal number of touchpoints to maximize conversions, and potential campaign cost save

Multi Touch Attribution (MTA) de-duplicates user IDs across all channels, providing a more accurate, customer level view of the conversion journeys.