Curso de Google Analytics para principiantes en Barcelona

Nunca consideres el estudio como una obligación, sino como una oportunidad para penetrar en el bello y maravilloso mundo del saber. Albert Einstein.

Una de las virtudes de saber, es que nos gusta que otros sepan también. Para nosotros no hay nada más importante que el conocimiento. Bien a través del obtenido por los datos, o bien a través de la transmisión de lo que mejor sabemos hacer: manejar datos y herramientas.

Con este propósito, abrimos nuestro primer curso de Google Analytics para principiantes (o Google Analytics 101). En realidad, Google Analytics, como herramienta que es, es una excusa para meternos en el apasionante mundo de la analítica digital. La analítica digital es la ciencia que mide las interacciones de los usuarios con nuestro site o App. El propósito final de la analítica digital es la de aportar una base sólida basada en datos para la optimización de la experiencia de usuario para que el usuario realice la acción pretendida (compra, registro, descarga, etc.) con la mayor eficiencia y eficacia posibles. La analítica digital toma datos de diversas fuentes, algunas de ellas cualitativas y otras cualitativas, y toma a los usuarios desde cualquier canal (aproximación multi u omnicanal), algunas de ellas online y otras offline. Como vemos, al final estamos hablando de la aproximación 360 al comportamiento e interacción de los usuarios con nuestro producto digital.

A medida que los negocios, los mercados y la tecnología evolucionan, también lo hacen los requisitos del analista digital. No es difícil ver que es un perfil cada vez más demandado (y poco a poco mejor pagado). Así que, tanto si ya se trabaja en analítica digital como si es el primer aterrizaje en ella, la formación en este ámbito es un paso que no podemos obviar.

Por supuesto, cuando hablamos de analítica digital no podemos dejar de pensar en Google Analytics. Líder indiscutible durante años entre las herramientas de analítica digital, Google Analytics ofrece, a partir de diversos grados de implementación (siempre escalable), una gran variedad de informes y datos a partir de los cuales podemos comenzar a optimizar nuestros productos digitales.

Cuesta mucho trabajo imaginarse la analítica digital sin la presencia rutilante de Google Analytics. Este curso va sobre dominar Google Analytics desde sus más tiernos cimientos. A partir de ahí, nos podremos convertir en ninjas no sólo de Google Analytics sino de la analítica digital, hasta encontrar la mejor herramienta para nuestras necesidades. En este momento, Google Analytics ofrece tres grandes ventajas:

Tiene una versión gratuita plenamente funcional (también ofrece versiones de pago). No hace falta hacer ninguna inversión en la herramienta en sí para poder comenzar a disfrutar de una infinidad de informes.


La implementación básica es tremendamente sencilla (y de ahí al infinito). Con sólo copiar y pegar un trozo de JavaScript en todas tus plantillas, podrás comenzar a recabar datos de manera inmediata.


Tiene el sello de Google: penetración en el mercado, recursos, documentación, formación, etc. En la red de usuarios de Google Analytics podremos encontrar infinidad de recursos, foros, add-ons, etc.


Sencillamente, no podemos imaginar un mejor aterrizaje en la analítica digital que aquel hecho a través de Google Analytics.

¿Qué vas a encontrar en este curso?

El curso está orientado en cuatro grandes bloques: principios de monitoraje, adquisición de usuarios, comportamiento de usuarios y objetivos.

1. Comenzando con Google Analytics:
1. ¿Cómo funciona Google Analytics?
2. Métricas y dimensiones.
3. ¿Cuáles son las métricas que me importan?
4. Introducción a la segmentación.
2. Adquisición: ¿cómo llegan mis visitas?
1. ¿De dónde llegan mis visitas y mis visitantes?
2. Tráfico Directo, Referentes, SEO y SEM.
3. Marcando campañas de Emailing y Afiliados.
4. Agrupación de canales.
3. Comportamiento: ¿qué hacen en mi site?
1. ¿Qué páginas y contenidos han mirado?
2. ¿Por dónde entraron al site? ¿Por dónde lo han abandonado?
3. ¿Qué buscaron internamente?
4. Agrupación de contenidos.
4. Objetivos: ¿hicieron lo que pretendemos?
1. Definir objetivos: micro y macro.
2. Reportes de objetivos.
3. Reportes de E-Commerce.

Este primer vistazo a la analítica digital a través de Google Analytics consta de dos sesiones consecutivas de tres horas cada una. El contenido del mismo es 100% práctico y usaremos una implementación muy completa con una gran cantidad de datos reales. El precio del curso es de 120€, y será ofrecido en nuestras oficinas, en Sant Cugat del Vallès.

¿Te lo vas a perder? ¿Vas a perder la gran oportunidad de entrar en el apasionante mundo de la analítica digital? Clica aquí para más información. Las plazas son limitadas.

¡La oportunidad de entrar por la puerta grande en la analítica digital está en frente de ti!

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Bed & Breakfast Analytics: lanzamos blog en castellano

Si hablas a una persona en una lengua que entiende, las palabras irán a su cabeza. Si le hablas en su propia lengua, las palabras irán a su corazón. Nelson Mandela.

Hace tiempo que veníamos pensando en abrir una sección en Bed & Breakfast Analytics para los castellano-parlantes. Después de mucho trabajo en distintos ámbitos, nos hemos lanzado a postear (lo siento, aquí no hemos encontrado una traducción que nos convenciera) en castellano. Eso sí, fieles a nuestro estilo.

Como buen post fundacional, queremos hablar de quiénes somos y qué hacemos. Nuestro blog, al que hemos llamado Bed & Breakfast Analytics es el blog de nuestra compañía, Ducks|in|a|row. Somos una consultoría de reciente creación, que se especializa en la gestión efectiva y eficiente de los datos. Nuestros servicios incluyen: análisis predictivo, analítica de clientes, analítica digital, optimización de la tasa de conversión, reporting, gestión de los flujos de información, optimización de procesos y formación.

El eje central que une todos estos puntos es el de los datos. Todas las compañías tienen datos. Muchas veces están diseminados en diversos sistemas que, muy frecuentemente, hablan lenguajes diferentes que difícilmente se entienden. De esta manera, la consolidación de las diversas fuentes de información, en caso de ser posible, se acaba realizando manualmente. El resultado final de esto es una inversión considerable de tiempo en la manipulación de los datos, y no en su análisis, que es lo único que lleva a la accionabilidad de los mismos. Si un análisis, un informe, un gráfico o un dato suelto no lleva en última instancia a un acción o decisión, entonces estaremos perdiendo el tiempo. Este es el gran foco de Ducks|in|a|row: generar acción de manera eficiente.

¿Qué quiere decir eficiente? En un artículo de Fortune de principios del 2016, se mencionan tres grandes problemas en la gestión de datos. Analicémonos con algo de detalle, aunque volveremos a este punto en futuros posts.

Alrededor del 80% de las grandes compañías han visto como alguna decisión estratégica de importancia se ha ido al garete por culpa de datos erróneos. Esto suele pasar cuando las fuentes de datos son inestables o deben manipularse frecuentemente y de manera manual.

Un 72% de tales empresas han experimentado retrasos en los tiempos de entrega de información a las personas que tienen que tomar las decisiones. Ésta es una consecuencia obvia de la desestrucutración de los datos, de un exceso de manipulación (y falta de análisis) y de una estrategia de reporting no existente o mal implementada.

Sólo un 27% de los directores cree que su compañía hace un uso efectivo de los datos, y un 32% piensa que las montañas de datos han hecho que las cosas vayan a peor. Éste es un síntoma que hemos notado muy frecuentemente: se tiende a invertir mucho tiempo (y dinero) en recolectar datos y muy poco a consolidarlos o analizarlos.

Siempre se ha dicho que los datos son el nuevo petróleo. Efectivamente, se puede extraer mucha información de los datos, pero, al igual que el petróleo, hay que definir bien los procesos de refinación. En el caso de los datos, esto hace referencia a procesos de automatización de limpieza, consolidación, reporting y análisis.

En definitiva, no es fácil hacer un uso efectivo de los datos, pero es posible. Nosotros podemos ayudarte.

Espero que nos acompañes leyéndonos. ¡Iremos publicando tan frecuentemente como nuestros quehaceres nos lo permitan!

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New course: Google Analytics for beginners.

The essence of training is to allow error without consequence. Orson Scott Card.

At Ducks|in|a|row we are excited. We are about to offer our very first course: Google Analytics 101. This course is intended to cover the first contact with the tool: how the interface works, which are the most common reports, how to read them, and, most importantly, why do we measure.

In any case, we should not forget that Google Analytics is just a tool. It is not an end by itself, but an instrument allowing us to reach such end. The end itself is Digital Analytics. The definition I like the most is the following:

"Digital Analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes, bot online and offline."

Google Analytics mainly helps driving insights from quantitative data. Google Analytics measures users' behavior in terms of a relation between dimensions and metrics in a temporal context. Data for qualitative analysis must be gathered from other sources: panel groups, surveys, chat, customer care data, etc.

The ultimate goal of both, qualitative and quantitative analysis, is to learn and to derive action: understand what works, what does not, and what worths a try. However, too much data (and less analysis) leads to data paralysis. Digital Analytics is about analyzing and organizing data so that actions can be actually derived and properly measured. We already talked about this in an older post. 

When implementing the actions, we should not be afraid of failing. Just be afraid if you don't learn from such mistakes, and make sure you react fast. Failure is tolerated. Idleness is not.

One last thought before explaining the contents of the course: data can tell you whatever you want if you torture it enough. This means that the analysis that will come out of Google Analytics' data will still go through the QA process, as well as the conclusions reached out of it. Whatever interpretation you extract can be as well biased or even wrong. Again, implement, measure, and react fast. Very fast.

The course itself is an introduction to Digital Analytics. Google Analytics is a way of touching that data and techniques. We will cover basic Digital Analytics terminology, such as KPI, metric, dimension filter, and segment, and how they are obtained in Google Analytics. We will learn about traffic sources, content consumed, and goal measurement. However, the most important topic acts as an umbrella for these topics: why do we measure? What do we want to accomplish by tagging a site or an App?

Register quick as places are limited. You will get more information here.

Good measuring!

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New partnership with Visual Website Optimizer: taking A/B testing and personalization to the next level.

Front-end processes optimization must include A/B and multivariate testing. You should be doing A/B testing. Ducks|in|a|row makes it much easier for you know!


We are very proud to announce that we have closed a partnership with Visual Website Optimizer for Spanish-speaking regions (Spain and LATAM). This will allow Visual Website Optimizer to consolidate its presence in Spanish-speaking regions by having experienced consultants offering services in local language and local support. At the same time, this will allow Ducks|in|a|row to consolidate its services of Conversion Rate Optimization, UX, and Digital Analytics, by offering services via one of the most used tools in the world for A/B testing. Actually, is one of the best tools for Conversion Rate Optimization.

What is Visual Website Optimizer?

It's an easy to use A/B testing tool that allows marketing professionals to create different versions of their websites and landing pages using a point-and-click editor (no HTML knowledge needed) and then see which version produces maximum conversion rate or sales. Integrating the split testing software is dead-simple: copy-paste a code snippet in your website once and you are ready to go live.
Visual Website Optimizer is also a flexible multivariate testing software (full factorial methodology) and has number of additional tools like behavioral targeting, heatmaps, usability testing, etc. With 100+ features in Visual Website Optimizer, you can be sure that all your conversion rate optimization activities are covered by our product.

Very quick: what is an A/B test?

Imagine you have detected some issues in your website. For instance, a low product-to-cart rate, that is, lots of users actually see your product descriptions, but very few add the into the cart. You can then start listing your hypothesis on why is that actually happening. A/B testing allows you to prove or disprove such hypothesis by showing several versions of the same content to different users. In this ways we can dismiss any seasonal effect that could affect your website if the changes are not tested in this way. Concretely:

By applying statistical methods we can determine which version behaves better with respect the established goals (purchase, add-to-cart, time on page, etc.).

The A/B testing cycle goes as follows:

Let's go step by step:

1. Opportunities: what to test? It could be a section of your website that is not properly working or just another section you want to leverage. It could be a single element of the page (a button), the entire page, or an entire process. This information can come from different levels: web tracking tools, heatmaps, surveys, etc., and it's fully linked to the knowledge of the business.

2. Expected turnover: what do you expect to have in return?

3. Priorization: with respect the technical needs and the expected turnover, you must score each possible test.

4. Objectives and segments: who is going to take part of the test? Are all users? Only new users? Only users who spent more than 1 minutes in your website? What should be considered a success? Is it a purchase? Is it viewing more than 3 pages of the website?

5. Implementation: actually inserting the tags in the website and make sure each user sees only one version of the experiment (this is a basic step in order for the math to be correctly applied).

6. Follow-up: was the test successful? Do we have a clear winner? Should we restate the terms of the test?

We will be giving more detailed information and tips for A/B testing in coming posts!

If you want to know more about the services of Ducks|in|a|row with respect A/B testing and Conversion Rate Optimization, click here.

If you want to know more about Visual Website Optimizer, click here.

We hope this partnership will bring Visual website Optimizer and Ducks|in|a|row a great value by having a great tool served by great consultants with very happy customers!

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A truth behind the lead business: a comprehensive approach (or the onion approach).

We are obsessed with improving our conversion rates while keeping our traffic levels. But, is this the right approach? I would say yes and no, bust mostly no. Let's see why.

Lead business is a very complex one. And, as we said in our initial post, complexity matters!

Before jumping in to a discussion regarding leads, let me recall a situation we faced time ago when doing consultancy for an e-retailer (a very big one, by the way). They were having a wonderful situation: around 20 million monthly sessions with around 3% session-to-order conversion rate, very high average order value, and pretty good margins. We were able to close a meeting and we offered them the full artillery: UX enhancements, fine tuning of the tracking tool continuous A/B testing, heatmaps, and so on. They told us: no. Surprised by this answer we asked: Why not? We were offering them a very good deal: very low fix fee and a high variable based on success (we did that because the site was horrible: we already identified couple of opportunities that could improve the conversion rate). They replied: we don't have the enough logistics resources to handle the extra amount of orders that would come! Ok. Lesson learned.

Some years later we came with a similar case. The context: traditional insurances company (life, car, and home) starting developing a digital presence. They had a horrible website that was generating around 500 leads per week. As soon as we saw it we wanted to offer the same service we offered years ago: conversion rate optimization, UX enhancement, full deployment of Google Analytics, etc. That is, we used to see the situation as:

However, as a side note, a comment from one of our analysts came to the deck. "Wait!" he said. This might not be the right approach. "Let's recall the e-retailer situation we faced years ago!". Indeed, let's take a picture of the current situation. Concretely, let's take the first step towards a comprehensive picture:

- 500 leads per week generated through the website.

- 2% lead-to-policy conversion rate (which takes place offline, via a phone call). This means 10 policies per week (simple math).

- To make 500 phone calls a week, they needed 2 full-time resources.

- The cost for a full-time resource was equivalent to the revenue generated by 4.5 policies. In other words, the "actual margin" is around one policy.

Now our landscape is broader, much broader!

With some basic enhancements to the website, we could increase leads by, let's say, 50% (site was really ugly). With more simple maths we say that we could have 750 leads per week generated through the website. But now the key point is that the lead-to-policy ratio would not necessarily change! This means that they would just increase the total number of policies up to 15! We always say that we need to ask the right question. At this point the right question to ask is: how many full-time resources do we need to handle 750 phone calls per week? A simple cross-multiplication shows us that we would need 3. This extra resource would cost us another 4.5 policies. In other words, the total cost for the 3 full-time resources would be 13.5 policies, so the actual margin would be equivalent to 1.5 policies! Wow! We did a lot of work to improve the website just to raise our margins by half a policy! This at the end means that, with the current set-up, the business does not look scalable. Or, in other words, more does not necessarily mean better. Even worse, what would happen if a sudden pike (seasonal pike, or a pike due to a sudden reduction of the prices, or by a sudden increase on your competitors prices, or by a sudden increase on traffic due, for instance, by an important investment on marketing) occurs? It's simple: the 3 full-time resources will not be able to handle all phone calls on time. In the insurances universe, if you don't handle a call soon you have high chances to loose it.

How do we solve this? Here we will apply the "onion strategy". Imagine your processes as an onion, and each one of the sub-processes are a layer of the onion. The basic idea behind this approach is that you should start optimizing processes from the end of the journey to its beginning. In this way, as the user flows he will always step towards a process that we already tried to optimize. In the case of the insurance company, the first thing we offered them was to understand whether we can improve the lead-to-policy rate. Here a new world appeared in front of our eyes: we taught them not to follow all leads (we implemented very cool models to prioritize the incoming leads), we taught them to catch the necessary data to understand why a lead converted or not into a policy, etc. At the end we were able to improve the lead-to-policy conversion rate up to 15%!. At that moment we were able to improve the website in order to bring more leads. And after that we were able to optimize the traffic sources, the prices, and to understand the competition, in order to have under control the total amount of sessions reaching the website. We got then a much broader landscape for the situation:

Despite this is a very simple representation of the full process that ranges from the intention to an actual policy, the exercise behind it is very insightful. And this is not only about data but about business. The full exercise of depicting the concrete steps through which the user flows up to becoming a client can only come from a deep knowledge of the business. Then, and only then, data science and tools appear. As we always say, the key point is to formulate the right questions. Then you find the data to answer them.

As a summary always keep in mind:

- More does not necessarily mean better.

- Broad your landscape until you have all the steps that can be improved or optimized.

- Think about the consequences the optimization of a step have to the whole process.

- Follow the onion approach: improve the latest steps of the whole process first and then improve backwards.

Good optimization!

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Chicken or the egg dilemma: tools or analysis, what does come first?

We need to install the tool X, said the CEO. How much does it cost, asks the CFO. Why do we need it, asks the analyst. There is not a best-tool-for-everything. Your own and unique context should determine the right tools for your company.

This post can be thought as a continuation of our previous post called "The broken pyramid, or the hungry hungry hippos game". In that occasion we discussed what happens when a company decides to stop the data-related processes at the reporting step and forget about further analyzing and predictive analysis. In this post we are going to discuss what happens when a tool is chosen without contemplating the scenarios for its usage. This is another major disaster. Let me recall four different stories I've faced in the last years.

The current tool is the worst you could ever have.

We were doing consultancy for an e-retailer in the European market. The company decided to open a new channel: Wines. For this sake, they hired an experienced Wine buyer and expert in its logistics (pretty fascinating, by the way). The guy, as we can imagine, had no experience in the digital world, and hardly used data for its strategy and daily operations (according to Avinash, these ones should be immediately fired). It was a promising start! Right after he joined I was explaining him how to use our BI tool (Qlikview). He did not seem impressed. When I asked him why he answered: "SAP is the best tool for this kind of things". Of course, we did not implement SAP. For the business complexity the company faced at that moment, Qlikview was more than enough. Actually, it still is. He never used Qlikview, he never used data. He was fired three months after.

The best-tool-according-to-the-CEO.

Another situation I can recall is when the CEO of another e-company came to me and said: I've been in a conference, and now I'm convinced this is the tool we need for A/B testing. I don't recall the name, but it was not one of the most used tools in the market. The tool was very complex to implement, the setup of every test was a nightmare and we hardly managed to implement a single complex test successfully. And, of top of that, it was very expensive. Result: no more A/B testing because "it's a framework that brings no added value" (sic). Pretty disappointing.

We have a great tool but lets try another one.

Another situation that needs to be avoided relates to the fact of changing of tool with no apparent reason. I remember another situation in which we were using Google Analytics for web tracking. The CIO came to us and said that he managed to get a free trial for another tool (Mixpanel). We were reluctant because the tool we were already using (fully deployed and operational) was enough for our present and short and mid-term needs. We were happy with it, users were empowered, lots of decisions were taken out of it, and we did not understand why we should try another one. We implemented Mixpanel while in parallel we were working with Google Analytics. Of course, we could not use all Mixpanel's features and the final result, according to the CIO was: "Mixpanel is a bad tool", which is totally inaccurate. The implementation of a tool requires time and focus. If you don't have them, don't start a new implementation.

I need this-and-that but I will use none.

Knowing and understanding the needs is as well a very complex task: we were doing that job for another company and I recall a CMO saying that he wanted a BI tool with real-time reporting capabilities. When I asked him why, he was not able to answer. He just wanted it, despite having no resources and no process defined for reacting based on information that arose from real-time data collection. Result: we implemented something (very expensive) that can be translated as "real-time". It was hardly used. After tons of money spent and an extremely complex implementation, we came with a tool that had lots of features but were, by far, underexploited.

Based on these four (horrible) experiences, we can tell that the tool is the result of your needs, and it deeply depends on your own and unique context. The opposite will hardly work. This, however, is a very complex process, and you need to proceed thoroughly in order to get the best tool possible for your case. Communication with the stakeholders is the key: know their needs, know the current status, know a roadmap for the short and mid-term, etc. If you can't do this by your own, I strongly recommend you to hire a consultant to do this job with an independent view. 

To summarize, take always into account your context in the moment of deciding which tool to use:

- Talk to every potential user of the tool.

- Establish realistic needs.

- Stick to the tool unless a new set of realistic needs appears and your current tool can't fulfil it.

And, last but not least, remember the 90/10 rule: 10% of your budget for tools, 90% for people exploiting them.

Good hunt! There is always a right-tool if you understand your context.

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Beyond selling products: listings and GA's enhanced e-commerce

Sell more. More bookings. Leverage the best-sellers. Everywhere. Is it the right tactic? Probably there is something smarter you can do to sell better.

I'm one of those still subscribed to many newsletters. Today I open one of them, from an online retailer, entitled "Check our best-sellers". I click over it and find, with not-so-big surprise that the list of products (five, and hardly more than five) is listed everywhere. I can imagine the situation: some Manager thought that the best way to proceed was to list the best-selling products everywhere. In this way what we achieve is not a best-sellers strategy but an only-seller strategy (or few-seller strategy).

Here we reach the key point of today's post: product lists is the new kid on the block to optimize. So far we use to optimize our pricing, communication, and merchandizing strategy based on the sales performance of a given product. Maybe, in a second iteration we include traffic (sessions, users, or even pageviews - or unique pageviews) and conversion rates. A traditional visualization for this is to relate the bookings and the pageviews generated by a product and mark four different areas:

- Cash-cows (low pageviews, high bookings)

- Stars (high pageviews, high bookings)

- Normal (average pageviews, average bookings)

- Problematic (high pageviews, low bookings)

Several variations can be also considered: revenue instead of bookings, pageview-to-unit conversion rate instead of pageviews, etc. But still, we use to consider the product as a silo without considering where it was seen or listed in the website, or even where has it been added to the cart from. So, let's take for example two products, one falling in the cash-cow category (few pageviews, high bookings) and another one falling in the Problematic category (high pageviews, low bookings). At this point we can formulate some questions regarding those pageviews generated:

- From which devices?

- From which traffic sources?

And, the new one:

- Where in our website or app was this product seen? Was it in the homepage? Was it at as a result of a search? Are there differences on performance depending where on our website we show the product? Does it make sense to promote those products in the same places through our website or app?

It might be the case that product A (the cash-cow) is listed in the homepage and, for example, in a category overview, and that product B (the problematic one) is listed only in the homepage. In this case we should analyze the ratio of sales (or cart entries) and impressions, segmented by the location in the website or app where the product impression took place.

This topic gets more importance as the concept of list is rapidly spreading over the businesses. The traditional concept of category is being replaced by meta-categories, by personalization, by searches and refinements, etc. The concept of category pages and the concept of static product showing is gone: lists are replacing them. Furthermore, lists are dynamic as a result of our own browsing experience and personalization efforts.

Fortunately, tools are very aware of this, and this time we want to go through the basics of Google Analytics' Enhanced E-commerce. You will find the full information about the reporting capabilities and technical instructions here. To give you a glance of the kind of reports you can find, we can state:

• Goal funnel analysis: how users flow through your goal funnel, where did they abandoned it, and how many re-entered it.
• Checkout behavior analysis: how users flow through the different steps of your checkout process, how many abandon it, and in which steps. The interesting part of this is that you can later create segments of users based on their behavior on the checkout. For instance, those who reached step 1 but not step 2!
• Product performance: these are the reports that relate sales performance (bookings, transactions, quantity, etc) with shopping behavior (product listings views, product details views, product adds to carts, product removals, and their correspondent rates).

This feature of Google Analytics allow us to understand better how a product performs also in terms of intention of purchase by relating some concepts of its merchandizing (such as lists and internal promotions) with its sales performance. This will help us to find the better placement(s) for a product, or a set of products in our website or app.

As always, the power of this tool also relies on the segmentation capabilities. Understanding the lists' behavior over different devices, user type, traffic source, etc., allow us to serve a better user experience. Placing the right product in the right place is a must-have on your merchadizing strategy, and it's something that traditional off-line retailers have been doing for a long time.

Just to finish, a plea to e-retailers: stop pushing products all through your website. Stop sending me every single product that can be purchased. More does not necessarily mean better. Every product has its right placement(s). You just need to find it (them). Enhanced e-commerce will allow you to do so.

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